{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:29:30Z","timestamp":1777735770537,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["JCYJ20230807154300002"],"award-info":[{"award-number":["JCYJ20230807154300002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global\u2013local collaborative attention module and a spider web convolution module. The former enhances feature representation through bidirectional feature interaction and dynamic weight allocation, reducing false positives and missed detections. The latter introduces an asymmetric sampling topology and six-directional receptive field paths to effectively improve the recognition of irregular objects. Experiments on the Cityscapes, CamVid, and BDD100K datasets, collected using vehicle-mounted cameras, demonstrate that the proposed method performs excellently across multiple evaluation metrics, including mIoU, mRecall, mPrecision, and mAccuracy. Comparative experiments with classical segmentation networks, attention mechanisms, and convolution modules validate the effectiveness of the proposed approach. The proposed method demonstrates outstanding performance in sensor-based semantic segmentation tasks and is well-suited for environmental perception systems in autonomous driving.<\/jats:p>","DOI":"10.3390\/s25154776","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T16:41:54Z","timestamp":1754325714000},"page":"4776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["ACNet: An Attention\u2013Convolution Collaborative Semantic Segmentation Network on Sensor-Derived Datasets for Autonomous Driving"],"prefix":"10.3390","volume":"25","author":[{"given":"Qiliang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiwen","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221116, China"},{"name":"Shenzhen Research Institute, China University of Mining and Technology, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221116, China"},{"name":"Shenzhen Research Institute, China University of Mining and Technology, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengpeng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Mine Digitization Engineering Research Center of the Ministry of Education, Xuzhou 221116, China"},{"name":"Shenzhen Research Institute, China University of Mining and Technology, Shenzhen 518057, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/TIV.2023.3318070","article-title":"Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey","volume":"9","author":"Chib","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6469","DOI":"10.1109\/JIOT.2020.3043716","article-title":"Computing Systems for Autonomous Driving: State of the Art and Challenges","volume":"8","author":"Liu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7572","DOI":"10.1109\/JIOT.2021.3130054","article-title":"Autonomous Driving Security: State of the Art and Challenges","volume":"9","author":"Gao","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"22694","DOI":"10.1109\/TITS.2022.3207665","article-title":"Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks","volume":"23","author":"Muhammad","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sellat, Q., Bisoy, S.K., and Priyadarshini, R. (2022). Semantic segmentation for self-driving cars using deep learning: A survey. Cogn. Big Data Intell. A Metaheuristic Approach, 211\u2013238.","DOI":"10.1016\/B978-0-323-85117-6.00002-9"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Papadeas, I., Tsochatzidis, L., Amanatiadis, A., and Pratikakis, I. (2021). Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey. Appl. Sci., 11.","DOI":"10.3390\/app11198802"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation","volume":"39","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022, January 23\u201327). Swin-unet: Unet-like pure transformer for medical image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"197748","DOI":"10.1109\/ACCESS.2024.3521597","article-title":"Enhancing Road Scene Segmentation with an Optimized DeepLabV3+","volume":"12","author":"Ren","year":"2024","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, C., Wu, H., and Chen, P. (2022). An Improved Deeplabv3+ Semantic Segmentation Algorithm with Multiple Loss Constraints. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0261582"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1109\/TPAMI.2017.2708714","article-title":"Exploring Context with Deep Structured Models for Semantic Segmentation","volume":"40","author":"Lin","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108290","DOI":"10.1016\/j.patcog.2021.108290","article-title":"Contextual Ensemble Network for Semantic Segmentation","volume":"122","author":"Zhou","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TIP.2020.3042065","article-title":"CGNet: A Light-Weight Context Guided Network for Semantic Segmentation","volume":"30","author":"Wu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115090","DOI":"10.1016\/j.eswa.2021.115090","article-title":"DSANet: Dilated Spatial Attention for Real-Time Semantic Segmentation in Urban Street Scenes","volume":"183","author":"Elhassan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"154239","DOI":"10.1109\/ACCESS.2019.2949076","article-title":"Efficient Semantic Segmentation Using Spatio-Channel Dilated Convolutions","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","article-title":"A Review of Semantic Segmentation Using Deep Neural Networks","volume":"7","author":"Guo","year":"2018","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7887","DOI":"10.1109\/TMM.2024.3372835","article-title":"Boundary-Guided Lightweight Semantic Segmentation with Multi-Scale Semantic Context","volume":"26","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.neunet.2022.10.034","article-title":"BASeg: Boundary Aware Semantic Segmentation for Autonomous Driving","volume":"157","author":"Xiao","year":"2023","journal-title":"Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lyu, H., Fu, H., Hu, X., and Liu, L. (2019, January 22\u201325). Esnet: Edge-Based Segmentation Network for Real-Time Semantic Segmentation in Traffic Scenes. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803132"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.neucom.2022.11.075","article-title":"GDN: Guided down-Sampling Network for Real-Time Semantic Segmentation","volume":"520","author":"Luo","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_25","first-page":"101","article-title":"Semantic segmentation for autonomous driving","volume":"10","author":"Yang","year":"2023","journal-title":"Auton. Driv. Percept. Fundam. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107260","DOI":"10.1016\/j.engappai.2023.107260","article-title":"Image Semantic Segmentation Approach Based on DeepLabV3 plus Network with an Attention Mechanism","volume":"127","author":"Liu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019, January 16\u201320). Selective kernel networks. Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_32","unstructured":"Park, J., Woo, S., Lee, J.Y., and Kweon, I.S. (2018). Bam: Bottleneck attention module. arXiv."},{"key":"ref_33","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., and Liu, Z. (2020, January 13\u201319). Dynamic Convolution: Attention Over Convolution Kernels. Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, D., Hu, J., Wang, C., Li, X., She, Q., Zhu, L., Zhang, T., and Chen, Q. (2021, January 20\u201325). Involution: Inverting the Inherence of Convolution for Visual Recognition. Proceedings of the 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01214"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","article-title":"Semantic Object Classes in Video: A High-Definition Ground Truth Database","volume":"30","author":"Brostow","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., and Darrell, T. (2020, January 13\u201319). BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning. Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00271"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1007\/s11633-024-1393-8","article-title":"Vision Transformers with Hierarchical Attention","volume":"21","author":"Liu","year":"2024","journal-title":"Mach. Intell. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, J., He, T., Zhuo, W., Ma, L., Ha, S., and Chan, S. (2022, January 18\u201324). TVConv: Efficient Translation Variant Convolution for Layout-Aware Visual Processing. Proceedings of the 2022 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01222"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., and Yang, G. (2023, January 2\u20136). Dynamic Snake Convolution Based on Topological Geometric Constraints for Tubular Structure Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5508","DOI":"10.1109\/TITS.2020.2987816","article-title":"NDNet: Narrow While Deep Network for Real-Time Semantic Segmentation","volume":"22","author":"Yang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1007\/s10489-021-02437-9","article-title":"Lightweight and Efficient Asymmetric Network Design for Real-Time Semantic Segmentation","volume":"52","author":"Zhang","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lo, S.-Y., Hang, H.-M., Chan, S.-W., and Lin, J.-J. (2019, January 15). Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation. Proceedings of the 1st ACM International Conference on Multimedia in Asia, Beijing, China.","DOI":"10.1145\/3338533.3366558"},{"key":"ref_48","unstructured":"Li, G., Yun, I., Kim, J., and Kim, J. (2019). DABNet: Depth-Wise Asymmetric Bottleneck for Real-Time Semantic Segmentation. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.neucom.2021.07.019","article-title":"LRDNet: A Lightweight and Efficient Network with Refined Dual Attention Decorder for Real-Time Semantic Segmentation","volume":"459","author":"Zhuang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"25489","DOI":"10.1109\/TITS.2021.3098355","article-title":"MSCFNet: A Lightweight Network with Multi-Scale Context Fusion for Real-Time Semantic Segmentation","volume":"23","author":"Gao","year":"2022","journal-title":"IEEE Trans. Intell. Transp. 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