DI-Net: Dynamic Interconnected Network for Accurate Industrial Defect Detection

Surface defect detection is crucial for ensuring product quality and improving production efficiency in real-world industrial scenes. Although deep learning-based detectors show remarkable performance, their effectiveness is hindered by the subtle distinction between defective objects and the background, as well as the varying sizes, shapes, and dense distribution of defects in industrial environments. Focusing on the above problems, this paper proposes a notable and innovative one-stage detector named Dynamic Interconnected Network (DI-Net). Firstly, the DI-Net consists of a dedicated Cross-Field Learning (CFL) module that leverages both convolution and involution feature extraction capabilities, enabling it to aggregate key information while avoiding over-fitting caused by excessive stacking of convolution layers, thus resolving the challenge of distinguishing subtle differences between the object and background. Secondly, we design the Dynamic Feature Fusion (DFF) Block, which is integrated into the neck network of DI-Net. This block employs flexible and adaptive dynamic convolution, enhanced by the CFL attention mask to further enrich feature representations, which enhance the model's detection performance for multi-size and shaped defect objects. In this paper, we refer to the overall module, which includes CFL and DFF, as the Dynamic Interconnected Module (DIM). To verify DI-Net's superiority and generalization, the proposed approach is tested on two sets of benchmark datasets including PKU-PCB and NEU-DET for industrial quality inspection. The experimental results demonstrate that our DI-Net achieves 80.9% and 95.6% mAP on the NEU-DET and PKU-PCB test datasets, respectively. Overall, it is a significant improvement for the detection performance at complex industrial, which better satisfies the accuracy requirements for the practical industrial applications.