Lightweight Detection of Metal Workpieces Based on YOLOv8

Currently, in the field of industrial parts sorting, traditional methods for sorting metal parts often depend on manual labor, resulting in low efficiency and high costs. Conventional object detection algorithms exhibit high complexity However, in the production processes of many small and medium-sized enterprises, these algorithms often fail to meet the computational power required for object detection, posing challenges for implementing parts sorting solutions based on object detection. To address this challenge, we propose a novel object detection algorithm, YOLOv8n-VLAG, derived from YOLOv8n. The model incorporates the VanillaNet network as the backbone feature extraction network, significantly reducing the parameter count of YOLOv8n. Simultaneously, it introduces the LSKA attention mechanism, enhancing accuracy while reducing the parameter count. Additionally, GhostConv is introduced in the head section, further decreasing the parameter count while enhancing performance and maintaining nearly unchanged computational complexity. Extensive experiments were conducted on a self-curated industrial parts dataset. The model’s parameters and computational complexity amounted to $1.36 \times 10^{\wedge} 6$ and 4.5 GFLOPs, respectively, representing only $45.3 \%$ and $55.6 \%$ of yolov8n. The average accuracy experienced a slight improvement, while the mAP decreased by only $0.4 \%$, resulting in a reduction in the number of parameters and computational complexity at a minor expense. This study offers a viable solution for the detection of lightweight parts.