A Lightweight Fine-Grained Perception Defect Inspection Network Under Multitask Learning on Highly Reflective Surface

As an important mechanical transmission device, the camshafts with highly reflective surface (HRS) are widely used in various automobiles. The automobile manufacturers have increasingly strict requirements for the processing quality of camshafts. However, affected by multiple processing steps, it is impossible to achieve 100% defect free products, which even tiny defects on their surfaces can significantly impact the performance, accuracy, and longevity of the camshaft. Therefore, precise inspection of camshaft defects in HRS becomes a necessary means of quality control, that reliable inspection methods still cannot meet the requirements. To address these issues, based on a multi-task learning framework, a lightweight fine-grained perceptual defect inspection network is proposed that is able to optimize both the saliency detection and classification tasks. The network utilizes a reparameterization technique and a lightweight bottleneck layer that can encode and fuse multi-scale features efficiently, while a fine-grained feature-aware module combined with spatial attention and channel attention mechanisms effectively improved the classification accuracy. Based on the experimental validation, the proposed method can achieve a high Mathews correlation coefficient of 0.951 and real-time speed of 190 fps on a single GPU, while meanwhile demonstrates excellent performance and interpretability in HRS defect inspection and classification.