3Detection of Urban Underground Sewage Pipeline Based on YOLOv5-Ghostnet Lightweight Model

To detect anomalies in the sewer environment, we identified five common faults: pipe breakage, deformation displacement, accumulation, corrosion, and detachment. However, traditional target detection methods are often cumbersome, slow, and not efficient. To address these challenges, we proposed a new YOLOv5 lightweight detection technology that introduced feature extraction mechanisms from GhostNet, ShuffleNet, and MobileNet. These mechanisms significantly reduce the computation cost of the model and are suitable for comparative experiments. After introducing the GhostNet feature extraction mechanism, it can be observed that the amount of computation and the substantial decrease in the number of model parameters. The mean average precision also increased from 87.4% to 88.5%. This improvement maintains the detection accuracy while reducing the model's weight. Our research provides an essential reference for lightweight research in this field.