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In view of the characteristics of complex scenes, such as dim light, occlusion, and long distance, an improved YOLOv4-based vehicle view object detection model, VV-YOLO, is proposed in this paper. The VV-YOLO model adopts the implementation mode based on anchor frames. In the anchor frame clustering, the improved K-means++ algorithm is used to reduce the possibility of instability in anchor frame clustering results caused by the random selection of a cluster center, so that the model can obtain a reasonable original anchor frame. Firstly, the CA-PAN network was designed by adding a coordinate attention mechanism, which was used in the neck network of the VV-YOLO model; the multidimensional modeling of image feature channel relationships was realized; and the extraction effect of complex image features was improved. Secondly, in order to ensure the sufficiency of model training, the loss function of the VV-YOLO model was reconstructed based on the focus function, which alleviated the problem of training imbalance caused by the unbalanced distribution of training data. Finally, the KITTI dataset was selected as the test set to conduct the index quantification experiment. The results showed that the precision and average precision of the VV-YOLO model were 90.68% and 80.01%, respectively, which were 6.88% and 3.44% higher than those of the YOLOv4 model, and the model\u2019s calculation time on the same hardware platform did not increase significantly. In addition to testing on the KITTI dataset, we also selected the BDD100K dataset and typical complex traffic scene data collected in the field to conduct a visual comparison test of the results, and then the validity and robustness of the VV-YOLO model were verified.<\/jats:p>","DOI":"10.3390\/s23073385","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T03:16:46Z","timestamp":1679627806000},"page":"3385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["VV-YOLO: A Vehicle View Object Detection Model Based on Improved YOLOv4"],"prefix":"10.3390","volume":"23","author":[{"given":"Yinan","family":"Wang","sequence":"first","affiliation":[{"name":"China FAW Corporation Limited, Global R&D Center, Changchun 130013, China"}]},{"given":"Yingzhou","family":"Guan","sequence":"additional","affiliation":[{"name":"China FAW Corporation Limited, Global R&D Center, Changchun 130013, China"}]},{"given":"Hanxu","family":"Liu","sequence":"additional","affiliation":[{"name":"China FAW Corporation Limited, Global R&D Center, Changchun 130013, China"}]},{"given":"Lisheng","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China"}]},{"given":"Xinwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China"}]},{"given":"Baicang","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China"}]},{"given":"Zhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1080\/19427867.2021.1953896","article-title":"Effectiveness of Intelligent Transportation System: Case study of Lahore safe city","volume":"14","author":"Saleemi","year":"2022","journal-title":"Transp. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.tra.2022.08.022","article-title":"Trust and perceived risk: How different manifestations affect the adoption of autonomous vehicles","volume":"164","author":"Kenesei","year":"2022","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1177\/03611981211051338","article-title":"Overview of Intelligent Transportation System Safety Countermeasures for Wrong-Way Driving","volume":"2676","author":"Hosseini","year":"2022","journal-title":"Transp. Res. Rec."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4984","DOI":"10.1109\/TIP.2013.2281406","article-title":"Object Detection via Structural Feature Selection and Shape Model","volume":"22","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3013","DOI":"10.1007\/s12555-018-0017-x","article-title":"Autonomous Vision-based Object Detection and Safe Landing for UAV","volume":"16","author":"Rabah","year":"2018","journal-title":"Int. J. Control. Autom. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neucom.2020.05.086","article-title":"Adaptive and azimuth-aware fusion network of multimodal local features for 3D object detection","volume":"411","author":"Tian","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MIM.2014.6825388","article-title":"Camera as the Instrument: The Rising Trend of Vision Based Measurement","volume":"17","author":"Shirmohammadi","year":"2014","journal-title":"IEEE Instrum. Meas. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TITS.2015.2466652","article-title":"Adaptive Sliding-Window Strategy for Vehicle Detection in Highway Environments","volume":"17","author":"Noh","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 13\u201316). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Machine Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Machine Intell."},{"key":"ref_14","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016). R-FCN: Object Detection via Region-based Fully Convolutional Networks. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","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, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A. (2016, January 8\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Mark Liao, H.-Y. (2020). YOLOv4: Optimal Speed and Precision of Object Detection. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). CornerNet: Detecting Objects as Paired Keypoints. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_21","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Mark Liao, H.-Y. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative Adversarial Networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4230","DOI":"10.1109\/TITS.2020.3014013","article-title":"Vehicle Detection and Tracking in Adverse Weather Using a Deep Learning Framework","volume":"22","author":"Hassaballah","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1109\/TITS.2019.2961679","article-title":"GAN-Based Day-to-Night Image Style Transfer for Nighttime Vehicle Detec-tion","volume":"22","author":"Lin","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4099","DOI":"10.1109\/TITS.2020.3041278","article-title":"SA-YOLOv3: An Efficient and Accurate Object Detector Using Self-Attention Mechanism for Autonomous Driving","volume":"23","author":"Tian","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4151","DOI":"10.1109\/TITS.2020.3041679","article-title":"Feature Calibration Network for Occluded Pedestrian Detection","volume":"23","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_27","first-page":"1","article-title":"R-YOLO: A Robust Object Detector in Adverse Weather","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","unstructured":"Arthur, D., and Vassilvitskii, S. (2007, January 7\u20139). k-means plus plus: The Advantages of Careful Seeding. Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_30","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Doll\u00e1r, P., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 22\u201325). 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_34","unstructured":"Misra, D. (2019). Mish: A Self Regularized Non-Monotonic Activation Function. arXiv."},{"key":"ref_35","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_36","unstructured":"Ghiasi, G., Lin, T.-Y., and Le, Q.V. (2018, January 2\u20138). DropBlock: A regularization method for convolutional networks. Proceedings of the Con-ference on Neural Information Processing Systems, Montreal, Canada."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4743","DOI":"10.1007\/s10489-018-1238-7","article-title":"K-means properties on six clustering benchmark datasets","volume":"48","author":"Franti","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4503613","DOI":"10.1109\/TIM.2021.3065438","article-title":"YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving","volume":"70","author":"Cai","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3385\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:01:25Z","timestamp":1760122885000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/7\/3385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,23]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23073385"],"URL":"https:\/\/doi.org\/10.3390\/s23073385","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,23]]}}}