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摘要:
针对水下光学图像目标检测过程中由于水中光线衰弱严重、图像环境复杂和拍摄设备移动等造成的生物识别精度低的问题，提出了基于改进YOLOv5s的弱光水下生物目标实时检测算法YOLOv5s-underwater。针对弱光水下光线衰弱的问题，引入了限制对比度自适应直方图均衡(CLAHE)算法对输入图像进行预处理，解决了颜色失真和图像毛糙的问题。针对复杂的弱光水下图像环境，提出了快速空间金字塔池化(SPPF)模块，解决了水下物体区分度低和特征损失严重的问题。针对拍摄设备移动带来的场景和形态变化问题，提出了一种基于旋转窗口的Swin-Transformer模块，提高了模型的泛化能力。针对水下小目标，修改了网络模型结构，提高了小目标的检测能力。仿真和实验结果表明：所提算法相较于YOLOv5s检测精度提高30.7%，证明了算法的有效性。
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关键词:
- 弱光水下生物目标 /
- YOLOv5s /
- 限制对比度自适应直方图均衡 /
- 快速空间金字塔池化 /
- 旋转窗口
Abstract:A real-time detection method of low-light underwater biological target based on improved YOLOv5s, known as YOLOv5s-underwater, was proposed to address the issue of low biometric recognition accuracy caused by the significant attenuation of light in water, the complex image environment, and the movement of shooting equipment in the process of underwater optical image target detection. Firstly, to solve the problem of weak underwater light attenuation, the contrast-limited adaptive histogram equalization (CLAHE) algorithm is introduced to preprocess the input image, which solves the problems of color distortion and image roughness. Secondly, the spatial pyramid pooling fast (SPPF) module is proposed to solve the problems of low discrimination and serious feature loss of underwater objects in the complex low-light underwater image environment. Thirdly, a Swin-Transformer module based on the spin window is proposed to improve the generalization ability of the model. Finally, the network model structure is modified to improve the detection ability of small underwater targets. Simulation and experiment prove that the proposed method improves the detection accuracy by 30.7% compared with YOLOv5s. Results from experiments support the method’s efficacy.
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图 2 弱光水下图像经算法处理前后效果对比
Figure 2. Comparison of effect of shallow sea underwater image before and after algorithm processing
图 10 YOLOv5s（左）和YOLOv5s-underwater（右）的检测结果
Figure 10. Test results of YOLOv5s (left) and YOLOv5S-Underwater (right)
表 1 改进的YOLOv5s和YOLOv5s实验结果1
Table 1. Improved YOLOv5s and YOLOv5s experimental results 1
% 算法 精确度 平均精确度 召回率 F1 海参 海胆 扇贝 海星 水草 YOLOv5s 61.5 67.7 65.3 69.9 20.0 56.9 68.1 62.00 YOLOv5s+CLAHE 73.0 73.4 72.4 74.2 89.7 76.5 68.4 72.22 YOLOv5s+SPPF 63.8 68.1 62.9 71.4 88.7 71.0 66.9 68.89 YOLOv5s+CLAHE+SPPF 74.3 76.9 72.1 76.6 94.2 78.8 67.1 72.48 YOLOv5s+SPPF+ST 69.3 74.6 71.4 76.6 97.2 77.8 66.0 71.42 YOLOv5s+CLAHE+SPPF+ST 81.9 84.2 79.7 80.5 99.1 85.1 68.1 75.66 YOLOv5s-underwater 84.8 85.6 83.0 84.8 99.8 87.6 67.4 76.18 
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表 2 改进的YOLOv5s和YOLOv5s实验结果2
Table 2. Improved YOLOv5s and YOLOv5s experimental results 2
算法 $ P_m@0.5 $/% $ P_m@0.5:0.95 $/% 检测速度/
(帧·s−1)YOLOv5s 60.6 32.3 166.67 YOLOv5s+CLAHE 70.2 36.0 138.89 YOLOv5s+SPPF 68.1 35.3 247.73 YOLOv5s+CLAHE+SPPF 71.7 38.4 239.71 YOLOv5s+SPPF+ST 68.9 37.9 154.86 YOLOv5s+CLAHE+SPPF+ST 74.1 39.8 150.10 YOLOv5s-underwater 74.1 41.6 146.84
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表 3 改进的YOLOv5s和其他目标检测算法实验结果1对比
Table 3. Comparison of experimental results 1 of improved YOLOv5s and other target detection algorithms
网络模型 平均精确度/% 召回率/% $ {F_1} $/% 检测速度/(帧·s−1) Faster R-CNN 40.1 16.4 23.28 20.44 SSD 37.7 14.9 21.36 30.17 Mbv2-SSD 31.4 12.5 17.88 70.16
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表 4 改进的YOLOv5s和其他目标检测算法实验结果对比
Table 4. Comparison of experimental results of improved YOLOv5s and other target detection algorithms
网络模型 平均精确度/% 召回率/% $ {F_1} $/% 检测速度/(帧·s−1) CenterNet 41.9 93.38 YOLOv5m 67.1 56.7 61.46 83.33 YOLOv5l 77.7 64.0 70.19 52.63 YOLOv5x 83.1 70.9 76.52 32.26 YOLOv5s-underwater 87.6 67.4 76.18 140.84
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