Underwater Trash Detection Method Based on Improved YOLOv5
To address the limitations of underwater image acquisition such as insufficient light,high noise and unclear object recognition,which lead to the ineffectiveness of existing object detection algorithms,an underwater garbage object detection al-gorithm based on improved YOLOv5 is proposed.The purpose of the improved object detection algorithm is to achieve more accu-rate detection and removal of underwater plastic trash from the ocean.The improved algorithm containes some improvements:us-ing the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm to enhance data features,which reduces the diffi-culty of feature extraction and enables the network to be detected more flexibly and more accurately;introducing a parameter-free attention module SimAM,using the lightweight convolution method GSConv to enhance network extraction capability while reducing model computation;At the same time,multi-scale feature fusion detection is added to solve the problem of small target location of underwater debris.Numbers of experiments are conducted based on MarineTrash which is a self-built real underwater environmental litter dataset,the results show that the improved method has good performance,in which the accuracy is in-creased by 4.3 percentage points,the mAP is increased by 3.5 percentage points,the GFLOPs is reduced by 0.3,and the model weight is only 13.9 MB,which is 0.6 MB lower than the baseline.The research on the underwater trash detection algorithm based on the improved YOLOv5 provides sufficient technology for deploying and installing detectors in Autonomous Underwater Ve-hicles(AUVs)to achieve detection and automatic removal of marine underwater trash and maintain the marine ecosystem.
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维普
