改进YOLOv7的自动驾驶目标检测算法

doi:10.3778/j.issn.1002-8331.2306-0315

摘要/Abstract

摘要： 能否准确实时检测出道路上的车辆、行人等物体对自动驾驶车辆十分重要。针对自动驾驶场景下，车辆在行驶中存在的漏检，小目标检测效果差的问题，提出一种改进YOLOv7算法的自动驾驶目标检测算法。修改网络中扩充感受野的模块，减少感受野模块的大小，提高模型速度的同时增强对图片特征信息的提取能力。在主干网络输出端引入双层路由注意力机制，提高模型对小目标物体的检测性能。用EIOU损失函数替换算法原本的CIOU损失函数，将预测框与真实框的高度与宽度的差异最小化，加快模型收敛速度的同时达到更好的定位效果。实验结果表明：在KITTI数据集上，改进后的YOLOv7算法进行目标检测时，其mAP达到94.7%，在原YOLOv7算法上提升了3.1个百分点，并且在小目标物体检测上获得了更高的检测精度，有效提升了模型对小目标检测效果。

关键词: 自动驾驶, 小目标检测, YOLOv7, 注意力机制, 损失函数

Abstract: It is very important for autonomous driving vehicles to accurately detect objects such as vehicles and pedestrians on the road in real time. Aiming at the problems of missed detection and poor detection effect of small targets in the autonomous driving scene, this paper proposes an automatic driving target detection algorithm that improves the YOLOv7 algorithm. Firstly, it modifies the modules in the network to expand the receptive field, reduces the size of the receptive field module, and improves the speed of the model and enhances the ability to extract image feature information. Secondly, the paper introduces the BRA attention mechanism at the output of the backbone network to improve the model’s ability to small target objects. Finally, it replaces the original CIOU loss function of the algorithm with the EIOU loss function to minimize the difference between the height and width of the predicted frame and the real frame, and speeds up the convergence of the model while achieving better positioning results. The experimental results show that：on the KITTI dataset, when the improved YOLOv7 algorithm performs target detection, its mAP reaches 94.7%, which is 3.1 percentage points higher than the original YOLOv7 algorithm, and it has achieved higher detection accuracy in small target object detection. It effectively improves the model’s detection effect on small targets.

Key words: autonomous driving, small target detection, YOLOv7, attention mechanism, loss function

胡淼, 姜麟, 陶友凤, 张志坚. 改进YOLOv7的自动驾驶目标检测算法[J]. 计算机工程与应用, 2024, 60(11): 165-172.

HU Miao, JIANG Lin, TAO Youfeng, ZHANG Zhijian. Improved YOLOv7 Automatic Driving Object Detection Algorithm[J]. Computer Engineering and Applications, 2024, 60(11): 165-172.

参考文献

[1] MOZAFFARI S, AL-JARRAH O Y, DIANATI M, et al. Deep learning-based vehicle behavior prediction for autonomous driving applications: a review[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 23(1): 33-47.
[2] ZAIDI S S A, ANSARI M S, ASLAM A, et al. A survey of modern deep learning based object detection models[J]. Digital Signal Processing, 2022, 126: 103514.
[3] VIOLA P, JONES M J. Robust real-time face detection[J]. International Journal of Computer Vision, 2004, 57(2): 137-154.
[4] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 886-893.
[5] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
[6] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
[7] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
[8] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 21-37.
[9] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2980-2988.
[10] ZHAO L, LI S. Object detection algorithm based on improved YOLOv3[J]. Electronics, 2020, 9(3): 9030537.
[11] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv:1804.02767, 2018.
[12] YU J, ZHANG W. Face mask wearing detection algorithm based on improved YOLOv4[J]. Sensors, 2021, 21(9): 3263.
[13] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv:2004.10934, 2020.
[14] 杨国亮, 杨浩, 余帅英, 等. 改进YOLOv5的交通标志检测算法[J]. 计算机工程与应用, 2023, 59(10): 262-269.
YANG G L, YANG H, YU S Y, et al. Improved traffic sign detection algorithm for YOLOv5[J]. Computer Engineering and Applications, 2023, 59(10): 262-269.
[15] 戚玲珑, 高建瓴. 基于改进YOLOv7的小目标检测[J]. 计算机工程, 2023, 49(1): 41-48.
QI L L, GAO J L. Small object detection based on improved YOLOv7[J]. Computer Engineering, 2023, 49(1): 41-48.
[16] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object dete-ctors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.
[17] 赵元龙, 单玉刚, 袁杰. 改进YOLOv7与DEEPSORT 的佩戴口罩行人跟踪[J]. 计算机工程与应用, 2023, 59(6): 221-230.
ZHAO Y L, SHAN Y G, YUAN J. Wearing mask pedestrian tracking based on improved YOLOv7 and DeepSORT[J]. Computer Engineering and Applications, 2023, 59(6): 221-230.
[18] ZHU L, WANG X, KE Z, et al. Biformer: vision transformer with bi-level routing aten-tion[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 10323-10333.
[19] 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.
[20] 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.
[21] HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[22] LIANG S, WU H, ZHEN L, et al. Edge YOLO: real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 25345-25360.
[23] LIU J, CAI Q, ZOU F, et al. BiGA-YOLO: a lightweight object detection network based on yolov5 for autonomous driving[J]. Electronics, 2023, 12(12): 2745.
[24] 宋绍剑, 夏海姐, 李刚. YOLOv5的改进算法及其在自动驾驶多目标检测的应用研究[J]. 计算机工程与应用, 2023, 59(15): 68-75.
SONG S J, XIA H J, LI G. Research on improved YOLOv5 algorithm and its application in multi-object detection for automatic driving[J]. Computer Engineering and Applications, 2023, 59(15): 68-75.