Flaw-YOLOv5s: A Lightweight Potato Surface Defect Detection Algorithm Based on Multi-Scale Feature Fusion

Abstract

Accurate and rapid detection of potato surface defects is crucial for advancing intelligent potato sorting. To elevate detection accuracy as well as shorten the computational load of the model, this paper proposes a lightweight Flaw-YOLOv5s algorithm for potato surface defect detection. Firstly, Depthwise Separable Convolution (DWConv) is used to displace the original Conv in the YOLOv5s network, aiming to reduce computational burden and parameters. Then, the SPPF in the backbone network is replaced by SPPELAN, which combines SPP with ELAN to enable the model to perform multi-scale pooling and feature extraction, optimizing detection capacity for small targets in potatoes. Finally, the lightweight convolution PConv is used to introduce a new structure, CSPC, to substitute for the C3 in the benchmark network, which decreases redundant computations and reduces the model parameters, achieving a lightweight network model. Experimental results demonstrate that the Flaw-YOLOv5s algorithm obtains a mean Average Precision (mAP) of 95.6%, with a precision of 94.6%, representing, respectively, an improvement of 1.6 and 1.8 percentage points over the YOLOv5s network. With only 4.33 million parameters, this lightweight and efficient model satisfies the requirements for detecting surface defects in potatoes. This research provides a reference for the online detection of potato surface defects and deployment on mobile devices.