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摘要:
车载红外图像的目标检测是自动驾驶进行道路环境感知的重要方式。针对现有车载红外图像目标检测算法中内存利用率低、计算复杂和检测精度低的问题，提出了一种改进YOLOv5s的轻量型目标检测算法。首先，将C3Ghost和Ghost模块引入YOLOv5s检测网络，以降低网络复杂度。其次，引进αIoU损失函数，以提升目标的定位精度和训练效率。然后，降低网络结构下采样率，并利用KMeans聚类算法优化先验框大小，以提高小目标检测能力。最后，分别在主干网络和颈部引入坐标注意力（Coordinate Attention，CA）和空间深度卷积模块进一步优化模型，提升模型特征的提取能力。实验结果表明，相对于原YOLOv5s算法，改进算法的模型大小压缩78.1%，参数量和每秒千兆浮点运算数分别减少84.5%和40.5%，平均检测精度和检测速度分别提升4.2%和10.9%。
Abstract:Vehicle infrared image target detection is an important way of road environment perception for autonomous driving. However, existing vehicle infrared image target detection algorithms have defects, such as low memory utilization, complex calculation and low detection accuracy. In order to solve the above problems, an improved YOLOv5s lightweight target detection algorithm is proposed. Firstly, the C3Ghost and Ghost modules are introduced into the YOLOv5s detection network to reduce network complexity. Secondly, the αIoU loss function is introduced to improve the positioning accuracy of the target and the networks training efficiency. Then, the subsampling rate of the network structure is reduced and the KMeans clustering algorithm is used to optimize the prior anchor size to improve the ability to detect of small targets. Finally, coordinate attention and spatial depth convolution modules are respectively introduced into the Backbone and Neck to further optimize the model and improve the feature extraction of the model. The experimental results show that compared with the original YOLOv5s algorithm, the improved algorithm can compress the model size by 78.1%, reduce the number of parameters and Giga Floating-point Operations Per Second by 84.5% and 40.5% respectively, and improve the mean average precision and detection speed by 4.2% and 10.9%, respectively.
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Key words:
- autonomous driving/
- target detection/
- infrared image/
- lightweight/
- YOLOv5s
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图 3（a）普通卷积和（b）Ghost卷积（Φ为线性操作）
Figure 3.(a) Ordinary convolution and (b) Ghost convolution (Φ is a linear operation)
图 6数据增强结果。（a）Mosaic增强；（b）MixUp增强；（c）Copy-Paste增强
Figure 6.Data augmentation results. (a) Mosaic augmentation; (b) MixUp augmentation; (c) Copy-Paste augmentation
图 7几种不同算法的检测效果。（a）YOLOv3-tiny；（b）YOLOv4-tiny；（c）YOLOv5n；（d）YOLOv6-N；（e）YOLO7-tiny；（f）YOLO5s；（g）本文算法
Figure 7.Detection results of different algorithms. (a) YOLOv3-tiny; (b) YOLOv4-tiny; (c) YOLOv5n; (d) YOLOv6-N; (e) YOLO7-tiny; (f) YOLO5s; (g) proposed in this paper
表 1优化后先验框大小
Table 1.Optimized prior anchor size
特征图尺度 160×160 80×80 40×40 感受野大小 小 中 大 [6,8] [14,37] [35,94] 先验框 [7,19] [31,26] [96,68] [15,13] [50,37] [154,145] 
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表 2YOLOv5s和YOLOv5s-G轻量化性能对比
Table 2.Performance comparison of lightweight for YOLOv5s and YOLOv5s-G
Model t/hours Size/MB Params/M GFLOPs P(%) R(%) mAP(%) FPS YOLOv5s 48.77 13.70 7.02 15.8 87.1 69.8 80.8 119 YOLOv5s-G 30.25 7.46 3.68 8.0 86.1 66.3 77.5 137 下载:
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表 3不同损失函数性能对比
Table 3.Performance comparison of different loss functions
Model t/hours P(%) R(%) mAP(%) FPS YOLOv5s-G 30.25 86.1 66.3 77.5 137 YOLOv5s-G-EIoU 24.31 84.5 68.7 78.9 141 YOLOv5s-G-SIoU 24.62 85.8 67.2 77.8 139 YOLOv5s-G-αIoU 23.50 85.9 69.3 79.8 147 下载:
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表 4多尺度融合性能对比
Table 4.Performance comparison of multi-scale fusion
Model t/hours Size/MB Params/M GFLOPs P(%) R(%) mAP(%) FPS YOLOv5s-G-αIoU 23.50 7.46 3.68 8.0 85.9 69.3 79.8 147 YOLOv5s-G1-αIoU 26.89 8.60 3.75 9.6 86.0 73.6 83.6 125 YOLOv5s-G2-αIoU 24.56 2.73 0.95 7.2 84.5 72.8 82.9 154 YOLOv5s-G2-αIoU-KMeans 25.62 2.73 0.95 7.2 85.5 72.4 83 154 下载:
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表 5不同注意力机制性能对比
Table 5.Performance comparison of different attention mechanisms
Model t/hours Size/MB Params/M GFLOPs P(%) R(%) mAP(%) FPS YOLOv5s-G2-αIoU-KMeans 25.62 2.73 0.95 7.2 85.5 72.4 83 154 YOLOv5s-G2-αIoU-KMeans-SE 30.95 2.75 0.96 7.2 86.0 73.5 84.1 149 YOLOv5s-G2-αIoU-KMeans-ECA 26.06 2.73 0.95 7.2 85.5 73.8 84.2 145 YOLOv5s-G2-αIoU-KMeans-CBAM 28.21 2.76 0.96 7.3 85.7 73.4 84 135 YOLOv5s-G2-αIoU-KMeans-CA 28.62 2.76 0.96 7.3 86.6 73.6 84.3 139 下载:
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表 6空间深度卷积效果
Table 6.SPD-Conv effect
Model t/hours Size/MB Params/M GFLOPs P(%) R(%) mAP(%) FPS YOLOv5s-G2-αIoU-Kmeans-CA 28.62 2.76 0.96 7.3 86.6 73.6 84.3 139 YOLOv5s-G2-αIoU-Kmeans-CA-SPD 30.28 3.0 1.09 9.4 87.4 74.6 85.0 132 下载:
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表 7与其他先进算法对比
Table 7.Comparison with other advanced algorithms
Model Size/MB Params/M GFLOPs P(%) R(%) mAP(%) FPS SSD 186.0 23.70 115.7 68.9 55.7 63.2 88 EfficientDet 302.0 39.40 107.5 72.8 58.4 67.8 52 YOLOv4+GhostNet 150.3 39.30 25.6 81.1 66.9 77.7 112 YOLOv5-MobileNetV3 7.9 4.0 9.3 83.7 67.5 76.9 128 YOLOv3-tiny 16.6 8.67 12.9 79.3 54.9 62.9 175 YOLOv4-tiny 12.9 6.27 16.2 78.9 57.3 67.2 149 YOLOv5n 3.7 1.76 5.1 83.6 66.1 76.6 164 YOLOv6-N 9.3 4.30 11.1 84.8 71.5 80.3 208 YOLOv7-tiny 12.3 6.02 13.2 84.2 74.7 83.6 143 YOLOv5s 13.7 7.02 15.8 87.1 69.8 80.8 119 proposed in this paper 3.0 1.09 9.4 87.4 74.6 85.0 132 下载:
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