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A novel deep convolutional neural network algorithm for surface defect detection


 The surface defect detection (SDD) problem is one of the crucial techniques during production process, so it has become a key research area to control the quality of industrial products, which has been increasingly of greater interest to the researchers especially with the rapid development of artificial neural networks technology in recent years. Therefore, this paper proposes a novel deep convolutional neural network (DCNN) algorithm aiming at SDD. Firstly, a dense cross-stage partial Darknet (DCP-Darknet) backbone network is designed for feature extraction by optimizing cross-stage partial Darknet (CSPDarknet) through the idea of dense connections, which can not only enhance feature reuse but also greatly alleviate the over-fitting issue. Secondly, a new cross-stage hierarchy (CSH) module is presented combining the cross-stage feature fusion strategy and depthwise separable convolution technique for each node of the path aggregated feature pyramid network (PAN). Finally, an efficient channel attention (ECA) mechanism is introduced in PAN to construct a novel efficient channel attention path aggregated feature pyramid network (ECA-PAN). The experimental results on three surface defect datasets show that the mAP of this network is 2.63%, 5.48% and 1.16% which is higher than that of the baseline network, respectively. The proposed network outperforms not only the classical models but SOTA models, which indicates the proposed algorithm can achieve higher accuracy and speed with fewer calculation parameters. And what’s more, the proposed algorithm also has outstanding generalization ability.