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Identifying defects at different scales is a challenge in industrial defect detection. To solve this problem, many multi-scale feature fusion networks have been proposed to improve multi-scale target detection accuracy by fusing fine-grained information from shallow networks and semantic information from deep networks. This approach requires the introduction of extra&#x00A0;parameters. Thinking from another perspective, can the accuracy of multi-scale target detection be improved by fusing the feature information under different receptive fields? For this purpose, we designed a three-layer network structure called Trident-LK Net. our model uses convolutional kernels of different sizes (31, 25, 1) in the feature extraction phase and establishes cross-fusion connections. This omits the feature fusion part and greatly reduces the network parameters while obtaining a good detection accuracy. Finally we perform experiments on the neu-det dataset and the gc10 dataset to verify the feasibility of our idea. While keeping the number of parameters to a minimum, our model achieves competitive detection results on the neu-det dataset (76.9% mAP) and optimal on the gc10 dataset (63.55% mAP).

Trident-LK Net: A Lightweight trident structure network with large kernel for muti-scale defect detection