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Therefore, this paper proposes a novel deep convolutional neural network algorithm aiming at SDD. Firstly, a dense cross-stage partial Darknet backbone network is designed for feature extraction by optimizing cross-stage partial Darknet through the idea of dense connections, which can, not only enhance feature reuse but also greatly alleviate the overfitting issue. Secondly, a new cross-stage hierarchy 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 ECA PAN. The experimental results on three surface defect datasets show that the mean average precision 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 state-of-the-art models, which indicates the proposed algorithm can achieve higher accuracy and speed with fewer calculation parameters. And what is more, the proposed algorithm also has outstanding generalization ability.<\/jats:p>","DOI":"10.1093\/jcde\/qwac071","type":"journal-article","created":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T12:39:37Z","timestamp":1659098377000},"page":"1616-1632","source":"Crossref","is-referenced-by-count":74,"title":["A novel deep convolutional neural network algorithm for surface defect detection"],"prefix":"10.1093","volume":"9","author":[{"given":"Dehua","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Henan University , Zhengzhou, 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyuan","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Henan University , Zhengzhou, 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linlin","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian 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