Identification algorithm of sewer obstruction based on YOLOv8
In order to improve the cleaning efficiency of sewer pipe obstacles and the accuracy of pipeline obstacle recognition,an improved pipeline obstacle recognition algorithm based on YOLOv8 is proposed.By optimizing the YOLOv8 target detection model,the algorithm is made more suitable for obstacle detection in the complex environment inside the pipeline.Based on the network structure of YOLOv8,the PGI module proposed is introduced.The addition of auxiliary reversible branches and multistage auxiliary module in the module effectively alleviates the information bottleneck problem,as a result significantly reducing the loss of accuracy.The SCConv module is introduced to replace the C2f module to maintain the detection accuracy while realizing the lightweight of the model.The introduction of Focal-Modulation module improves the traditional SPPF module,so that the accuracy of the model is improved to a certain extent.Experimental results show that compared with YOLOv8n model,the improved recognition algorithm improves the accuracy of mAP@0.5 by 4.6%,improves the accuracy of mAP@0.5～0.95 by 3.9%,reduces the number of parameters by 33.3%,and reduces the amount of calculation by 17.3%.It is more suitable for the identification and detection of obstacles in sewer pipes.
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