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Firstly, the DySnakeConv module is employed in Backbone\u2013Neck cross-layer connections. With a dynamic structure and adaptive learning, it can capture the complex morphological features of spray codes. Secondly, we proposed an Attention-guided Shape Enhancement Module with CAA (ASEM-CAA), which adopts a symmetrical dual-branch structure to facilitate bidirectional interaction between local and global features, enabling precise prediction of the overall spray code shape. It also reduces feature discontinuity in dot-matrix codes, ensuring a more coherent representation. Furthermore, Slim-neck, which is famous for its more lightweight structure, is adopted in the Neck to reduce model complexity while maintaining accuracy. Finally, Shape-IoU is applied to improve the accuracy of the bounding box regression. Experiments show that DASS-YOLO improves the detection accuracy by 1.9%. Additionally, for small defects such as incomplete code and code spot, the method achieves better accuracy improvements of 8.7% and 2.1%, respectively.<\/jats:p>","DOI":"10.3390\/sym17060906","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T09:13:01Z","timestamp":1749460381000},"page":"906","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DASS-YOLO: Improved YOLOv7-Tiny with Attention-Guided Shape Awareness and DySnakeConv for Spray Code Defect Detection"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6958-6166","authenticated-orcid":false,"given":"Yixuan","family":"Shi","sequence":"first","affiliation":[{"name":"School of Physics Science and Information Engineering, Liaocheng University, Liaocheng 252000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3748-9366","authenticated-orcid":false,"given":"Shiling","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Physics Science and Information Engineering, Liaocheng University, Liaocheng 252000, China"},{"name":"Liaocheng Key Laboratory of Industrial-Internet Research and Application, Liaocheng 252000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiyue","family":"Bian","sequence":"additional","affiliation":[{"name":"School of Physics Science and Information Engineering, Liaocheng University, Liaocheng 252000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Physics Science and Information Engineering, Liaocheng University, Liaocheng 252000, China"},{"name":"Liaocheng Key Laboratory of Industrial-Internet Research and Application, Liaocheng 252000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5732-7213","authenticated-orcid":false,"given":"Lishan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Physics Science and Information Engineering, Liaocheng University, Liaocheng 252000, China"},{"name":"Liaocheng Key Laboratory of Industrial-Internet Research and Application, Liaocheng 252000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11469","DOI":"10.1021\/acsami.4c15957","article-title":"Advancements in Inkjet Printing of Metal-and Covalent-Organic Frameworks: Process Design and Ink Optimization","volume":"17","author":"Najafabadi","year":"2025","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106965","DOI":"10.1016\/j.measurement.2019.106965","article-title":"In-line inspection solution for codes on complex backgrounds for the plastic container industry","volume":"148","author":"Liang","year":"2019","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3755","DOI":"10.3934\/mbe.2021189","article-title":"Defect detection in code characters with complex backgrounds based on BBE","volume":"18","author":"Peng","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NE, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_6","first-page":"1","article-title":"Yolov3: An incremental improvement","volume":"Volume 1804","author":"Farhadi","year":"2018","journal-title":"Proceedings of the Computer Vision and Pattern Recognition"},{"key":"ref_7","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_8","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Wei, X. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao HY, M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_10","unstructured":"Wang, C.Y., Yeh, I.H., and Mark Liao, H.Y. (October, January 29). Yolov9: Learning what you want to learn using programmable gradient information. Proceedings of the European Conference on Computer Vision, Milan, Italy."},{"key":"ref_11","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). Yolov10: Real-time end-to-end object detection. arXiv."},{"key":"ref_12","first-page":"1261","article-title":"A review of metal surface defect detection based on computer vision","volume":"50","author":"Wu","year":"2024","journal-title":"Acta Autom. Sin."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Chen, W., Huang, X., and Yan, Y. (2025). Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering. Remote Sens., 17.","DOI":"10.3390\/rs17020193"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_16","first-page":"1","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kang, S., Hu, Z., Liu, L., Zhang, K., and Cao, Z. (2025). Object Detection YOLO Algorithms and Their Industrial Applications: Overview and Comparative Analysis. Electronics, 14.","DOI":"10.3390\/electronics14061104"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"117608","DOI":"10.1016\/j.measurement.2025.117608","article-title":"SFW-YOLO: A lightweight multi-scale dynamic attention network for weld defect detection in steel bridge inspection","volume":"253","author":"Luo","year":"2025","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"117410","DOI":"10.1016\/j.measurement.2025.117410","article-title":"A insulator defect detection network based on improved YOLOv7 for UAV aerial images","volume":"253","author":"You","year":"2025","journal-title":"Measurement"},{"key":"ref_20","first-page":"177","article-title":"Steel surface defect detection algorithm based on improved YOLOv8n","volume":"2","author":"Yao","year":"2025","journal-title":"J. Liaocheng Univ. Nat. Sci. Ed."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"200205","DOI":"10.1016\/j.amf.2025.200205","article-title":"YOLO-L: A High-Precision Model for Defect Detection in Lattice Structures","volume":"4","author":"Guo","year":"2025","journal-title":"Addit. Manuf. Front."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, J., Xie, X., Liu, G., and Wu, L. (2025). A Lightweight PCB Defect Detection Algorithm Based on Improved YOLOv8-PCB. Symmetry, 17.","DOI":"10.3390\/sym17020309"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., and Yang, G. (2023, January 1\u20136). Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tang, F., Huang, Q., Wang, J., Hou, X., Su, J., and Liu, J. (2023, January 13\u201315). DuAT: Dual-aggregation transformer network for medical image segmentation. Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV), Xiamen, China.","DOI":"10.1007\/978-981-99-8469-5_27"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cai, X., Lai, Q., Wang, Y., Wang, W., Sun, Z., and Yao, Y. (2024, January 16\u201322). Poly kernel inception network for remote sensing detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02617"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s11554-024-01436-6","article-title":"Slim-neck by GSConv: A lightweight-design for real-time detector architectures","volume":"21","author":"Li","year":"2024","journal-title":"J. Real-Time Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","article-title":"Enhancing geometric factors in model learning and inference for object detection and instance segmentation","volume":"52","author":"Zheng","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_28","unstructured":"Zhang, H., and Zhang, S. (2023). Shape-iou: More accurate metric considering bounding box shape and scale. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gao, S., Ren, W., and Hu, K. (2021, January 1\u20133). Swin Transformer and Mask R-CNN Based Person Detection Model for Firefighting Aid System. Proceedings of the International Conference of Artificial Intelligence, Medical Engineering, Education, Moscow, Russia.","DOI":"10.1007\/978-3-030-92537-6_4"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 16\u201322). Detrs beat yolos on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_31","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_32","unstructured":"Xu, W., and Wan, Y. (2024). ELA: Efficient local attention for deep convolutional neural networks. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Y.F., Ren, W., Zhang, Z., Jia, Z., Wang, L., and Tan, T. (2021). Focal and efficient IOU loss for accurate bounding box regression. arXiv.","DOI":"10.1016\/j.neucom.2022.07.042"},{"key":"ref_34","unstructured":"Gevorgyan, Z. (2022). SIoU loss: More powerful learning for bounding box regression. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J.Y., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_36","first-page":"1","article-title":"Triplet-graph reasoning network for few-shot metal generic surface defect segmentation","volume":"70","author":"Bao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","first-page":"292","article-title":"YOLOv7-BA: A Metal Surface Defect Detection Model Based On Dynamic Sparse Sampling And Adaptive Spatial Feature Fusion","volume":"Volume 6","author":"Ma","year":"2024","journal-title":"Proceedings of the 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)"},{"key":"ref_38","first-page":"1","article-title":"DF-YOLOv7: Steel surface defect detection based on focal module and deformable convolution","volume":"19","author":"Zhang","year":"2025","journal-title":"Signal Image Video Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1049\/ipr2.13086","article-title":"Research on surface defect detection model of steel strip based on MFFA-YOLOv5","volume":"18","author":"Chen","year":"2024","journal-title":"IET Image Process."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/6\/906\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:48:26Z","timestamp":1760032106000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/6\/906"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,8]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["sym17060906"],"URL":"https:\/\/doi.org\/10.3390\/sym17060906","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,8]]}}}