基于改进YOLOv5s的轻量化蟹塘障碍物检测与定位方法

蟹塘中不定期放置不同类型、不同大小的养殖装置影响无人作业船的自动巡航作业，为了提高无人作业船的工作效率和安全性，该研究提出一种改进YOLOv5s的轻量化蟹塘障碍物检测模型，并结合深度相机对蟹塘障碍物进行定位。改进模型从平衡检测速度和检测精度的角度出发，首先将ShuffleNetV2轻量化网络作为主干特征提取网络，大幅缩减模型体积；其次在不增加计算量的同时引入SENet注意力机制，加强对蟹塘障碍物目标的特征感知；接着将SPPF模块改进为SPPFCSPC模块，增强不同尺度下蟹塘障碍物的检测效果；最后采用SIoU损失函数加速模型收敛，提高检测的准确性。改进模型结合RealSense D435i深度相机获取的彩色图像对障碍物进行检测，并得到障碍物中心点在蟹塘坐标系下的三维坐标和障碍物的投影宽度。试验结果表明，改进模型对竹竿、蟹笼、增氧机3类障碍物具有良好的区分度和识别效果，与原始YOLOv5s模型相比，改进模型的参数量和计算量分别减小了62.8%和80.0%，模型大小仅为5.5 MB，单张图像的检测速度达15.2 ms，检测速度提升了44.5%，平均精度均值（mean average prec...

Lightweight crab pond obstacle detection and location method based on improved YOLOv5s

Obstacles have posed a great challenge to the unmanned working vessel in the crab pond. Once the obstacles appear on the route of the unmanned working vessel, it is easy to collide with the vessel. It is very necessary to quickly identify and locate the obstacles in the crab pond, in order to improve the efficiency and safety of the unmanned working vessel. In this study, an improved YOLOv5s model was proposed to detect the obstacles in the crab pond. The obstacles were then located to combine with the depth camera. Firstly, the lightweight network ShuffleNetV2 was used as the backbone feature extraction network, and the depth-separable convolution and channel mixing strategies were adopted to greatly reduce the model volume, in order to accelerate the detection speed for high accuracy. Secondly, the SE attention mechanism was introduced to enhance the feature perception of obstacles in the crab pond, particularly without increasing the amount of computation. Thirdly, the SPPF module was improved into the SPPFCSPC module. The target features were extracted under different receptive fields, in order to enhance the detection of obstacles in crab ponds at different scales. Finally, the SIoU loss function was adopted to further accelerate the model convergence for high accuracy. According to the color images obtained by the RealSense D435i depth camera, the obstacle was detected to locate the pixel coordinates of the center point of the obstacle in the coordinate system of the crab pond using coordinate conversion. The width of the obstacle was obtained simultaneously. The experimental results showed that the better recognition of the improved model was achieved in the pole, tarp, and aerator, where the confusion matrix output was observed by the model on the test set. The number of parameters and calculation amount of the improved YOLOv5s model were reduced by about 62.8% and 80.0% respectively, compared with the original. The model size was only 5.5 MB, and the volume was reduced by about 61.8%. The detection speed increased by 44.5%, and the inference speed of a single image reached 15.2 ms, where the mean average precision reached 93.3% for obstacles in the crab pond. Compared with the YOLOv5s-MobileNetV2, YOLOv5s-GhostNet, YOLOv7 and YOLOv8, the improved model had maintained the best balance in terms of parameter number, computation amount, detection speed and detection accuracy. A series of positioning and width measurement tests were conducted to verify the location accuracy of the improved model. Three typical obstacles were located in a crab pond at the fishery Science and Technology Demonstration base in Jintan District, Changzhou, Jiangsu Province. The test results showed that the average absolute error and average relative error of the distance between the three types of obstacles and the camera were 0.16 m and 2.26% in the range of 2-10 m, respectively, and the maximum absolute error and maximum relative error were 0.35 m and 3.74%, respectively. The width measurement errors of the pole, crab trap and aerator were concentrated in 0-0.05 m,-0.05-0.13 m and 0.01-0.19 m, respectively. The average errors were 0.03, 0.05 and 0.10 m, respectively, and the average relative errors were 34.1%, 7.5% and 5.0%, respectively. Finally, the depth camera was fixed in the front of the unmanned working vessel, in order to detect the three types of obstacles in the crab pond several times. The detection and positioning effects of the model were verified in the actual crab pond environment. The improved model was used to successfully detect the obstacles, in order to export the category, confidence, three-dimensional coordinate and width information. In summary, the improved model can fully meet the requirements to detect and locate the common obstacles in the crab pond environment. The finding can provide an important reference for the autonomous obstacle avoidance and cruise operation of the unmanned working vessel.