Vehicle Detection Algorithm Based on Improved YOLOv8 in Traffic Surveillance

doi:10.3778/j.issn.1002-8331.2310-0101

Abstract

Abstract: To address the current problems of insufficient vehicle detection accuracy and slow detection speed in complex traffic monitoring scenarios, a lightweight vehicle detection algorithm based on YOLOv8 model is proposed. Firstly, FasterNet is used to replace the backbone feature extraction network of YOLOv8, which reduces redundant computation and memory access, and improves the detection accuracy and inference speed of the model.Secondly, the SimAM attention module is added to the Backbone and Neck sections, which enhances the important features of the target vehicles without increasing the original network parameters, and improves the feature fusion capability. Then, to address the problem of poor detection of small-sized vehicles under dense traffic flow, a small target detection head is added to better capture the features and contextual information of small-sized vehicles. Finally, Wise-IoU, which can adaptively adjust the weight coefficients, is used as the loss function of the improved model, which enhances the regression performance of the bounding box and the robustness of the detection.The experimental results on the UA-DETRAC dataset show that compared with the original model, the improved method in this paper is able to achieve better detection accuracy and speed in the traffic monitoring system, with the mAP and FPS improved by 3.06 percengtage points and 3.36%, respectively, which effectively improves the problem of the poor detection of small-target vehicles in the complex traffic scenarios, and achieves a good balance between accuracy and speed.

Key words: vehicle detection, traffic surveillance, YOLOv8, small-target detection, attention mechanism

摘要： 针对目前复杂交通监控场景下车辆检测精度不足、检测速度慢的问题，提出一种基于YOLOv8模型的轻量级车辆检测算法。采用FasterNet替换YOLOv8的骨干特征提取网络，减少了冗余计算和内存访问，提高了模型的检测精度和推理速度；在Backbone和Neck部分添加SimAM注意力模块，在不增加原始网络参数的同时增强了目标车辆的重要特征，提高了模型的特征融合能力；针对密集车流下小尺寸车辆检测效果不佳的问题，添加小目标检测头，更好地捕获小尺寸车辆的特征和上下文信息；使用可自适应调整权重系数的Wise-IoU作为改进模型的损失函数，提升了边界框的回归性能和检测的鲁棒性。在UA-DETRAC数据集的实验结果表明，相较于原模型，改进方法在交通监控系统中能够达到较好的检测精度和速度，mAP和FPS分别提高了3.06个百分点和3.36%，有效改善了复杂交通场景下小目标车辆检测效果不佳的问题，并在精度和速度之间取得了很好的平衡。

关键词: 车辆检测, 交通监控, YOLOv8, 小目标检测, 注意力机制

ZHOU Fei, GUO Dudu, WANG Yang, WANG Qingqing, QIN Yin, YANG Zhuomin, HE Haijun. Vehicle Detection Algorithm Based on Improved YOLOv8 in Traffic Surveillance[J]. Computer Engineering and Applications, 2024, 60(6): 110-120.

周飞, 郭杜杜, 王洋, 王庆庆, 秦音, 杨卓敏, 贺海军. 基于改进YOLOv8的交通监控车辆检测算法[J]. 计算机工程与应用, 2024, 60(6): 110-120.

References

[1] GHAHREMANNEZHAD H, SHI H, LIU C. Object detection in traffic videos: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(7): 6780-6799.
[2] WANG Y, YANG G, GUO J. Vehicle detection in surveillance videos based on YOLOv5 lightweight network[J]. Bulletin of the Polish Academy of Sciences: Technical Sciences, 2022: 143644.
[3] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[4] 宋建辉, 王思宇, 刘砚菊, 等.基于改进FFRCNN网络的无人机地面小目标检测算法[J].电光与控制, 2022, 29(7): 69-73.
SONG J H, WANG S Y, LIU Y J, et al. Ground small target detection algorithm of UAV based on improved FFRCNN network[J]. Electronics Optics & Control, 2022, 29(7): 69-73.
[5] 李松江, 吴宁, 王鹏, 等.基于改进Cascade RCNN的车辆目标检测方法[J].计算机工程与用, 2021, 57(5): 123-130.
LI S J, WU N, WANG P, et al. Vehicle target detection method based on improved cascade RCNN[J]. Computer Engineering and Applications, 2021, 57(5): 123-130.
[6] 谢光达, 李洋, 曲洪权, 等.基于改进Transformer的小目标车辆精确检测算法[J].激光与光电子学进展, 2022, 59(18): 354-361.
XIE G D, LI Y, QU H Q, et al. Small target accurate vehicle detection algorithm based on improved transformer[J]. Laser & Optoelectronics Progress, 2022, 59(18): 354-361.
[7] SONG Y, HONG S, HU C, et al. MEB-YOLO: an efficient vehicle detection method in complex traffic road scenes[J]. Computers, Materials & Continua, 2023, 75(3): 5761-5784.
[8] 郭宇阳, 胡伟超, 戴帅, 等.面向路侧交通监控场景的轻量车辆检测模型[J].计算机工程与应用, 2022, 58(6): 192-199.
GUO Y Y, HU W C, DAI S, et al. Lightweight vehicle detection model for roadside traffic monitoring scenarios[J]. Computer Engineering and Applications, 2022, 58(6): 192-199.
[9] HUANG Z, WANG J, FU X, et al. DC-SPP-YOLO: dense connection and spatial pyramid pooling based YOLO for object detection[J]. Information Sciences, 2020, 522: 241-258.
[10] ZHANG D, ZHENG Z, LI M, et al. CSART: channel and spatial attention-guided residual learning for real-time object tracking[J]. Neurocomputing, 2021, 436: 260-272.
[11] ZHANG D, ZHENG Z, WANG T, et al. HROM: learning high-resolution representation and object-aware masks for visual object tracking[J]. Sensors, 2020, 20(17): 4807.
[12] ZONG Z, SONG G, LIU Y. Detrs with collaborative hybrid assignments training[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023: 6748-6758.
[13] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J]. arXiv:2207.02696, 2022.
[14] CHEN J, KAO S, HE H, et al. Run, don’t walk: chasing higher FLOPS for faster neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 12021-12031.
[15] HAN K, WANG Y, TIAN Q, et al. Ghostnet: more features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 1580-1589.
[16] 张寅, 朱桂熠, 施天俊, 等.基于特征融合与注意力的遥感图像小目标检测[J].光学学报, 2022, 42(24): 140-150.
ZHANG Y, ZHU G Y, SHI T J, et al. Small object detection in remote sensing images based on feature fusion and attention[J]. Acta Optica Sinica, 2022, 42(24): 140-150.
[17] YANG L, ZHANG R Y, LI L, et al. Simam: a simple, parameter-free attention module for convolutional neural networks[C]//International Conference on Machine Learning, 2021: 11863-11874.
[18] ZHENG Z, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2020: 12993-13000.
[19] ZHANG Y F, REN W, ZHANG Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146-157.
[20] TONG Z, CHEN Y, XU Z, et al. Wise-IoU: bounding box regression loss with dynamic focusing mechanism[J]. arXiv:2301.10051, 2023.
[21] WEN L, DU D, CAI Z, et al. UA-DETRAC: a new benchmark and protocol for multi-object detection and tracking[J]. Computer Vision and Image Understanding, 2020, 193: 102907.
[22] HOWARD A, SANDLER M, CHU G, et al. Searching for mobilenetv3[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 1314-1324.
[23] CHEN H, WANG Y, GUO J, et al. VanillaNet: the power of minimalism in deep learning[J]. arXiv:2305.12972, 2023.
[24] TAN M, LE Q. Efficientnet: rethinking model scaling for convolutional neural networks[C]//International Conference on Machine Learning, 2019: 6105-6114.
[25] WANG Q, WU B, ZHU P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11534-11542.
[26] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[27] OUYANG D, HE S, ZHANG G, et al. Efficient multi-scale attention module with cross-spatial learning[C]//2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023: 1-5.
[28] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13713-13722.
[29] MISRA D, NALAMADA T, ARASANIPALAI A U, et al. Rotate to attend: convolutional triplet attention module[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021: 3139-3148.
[30] LI X, HU X, YANG J. Spatial group-wise enhance: improving semantic feature learning in convolutional networks[J]. arXiv:1905.09646, 2019.
[31] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015.
[32] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[33] SANDLER M, HOWARD A, ZHU M, et al. Mobilenetv2: inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 4510-4520.
[34] TIAN Z, SHEN C H, CHEN H, et al. FCOS: a simple and strong anchor-free object detector[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(4)：1922-1933.
[35] ZHOU X, WANG D, KR?HENBüHL P. Objects as points[J]. arXiv:1904.07850, 2019.
[36] CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//European Conference on Computer Vision. Cham: Springer International Publishing, 2020: 213-229.