FLCNet: faster and lighter cross-scale feature aggregation network for lead bar surface defect detection

Abstract

Aiming at the defect inspection under the characteristics of scale change, high reflection, inclined deformation of defects of lead bars and meeting the needs for faster detection, this paper proposes a faster and lighter cross-scale feature aggregation network (FLCNet). In this study, we focus on the redundancy of channel information, and design a new partial channel group convolution, based on which we design a Faster C3 module and a lightweight cross-scale feature fusion module. In addition, we design a cross-scale slim neck to reduce the redundant feature transfer of the model. Finally, we propose a uniform brightness acquisition method for lead bar sidewall image by using combined light source and construct a lead bar dataset with various complex defect samples. Experiments show that FLCNet effectively improves the detection accuracy of the surface defects of lead bars, the mAP@0.5 value reaches 97.1%, and compared with YOLOv5s, the model’s parameters reduced by 33.9%. At the same time, the detection speed reaches 114.9 FPS, which is faster than other advanced detection models.