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Aiming at the problems of fuzzy and discontinuous edge contours extracted by traditional edge detection methods, a photovoltaic panel crack detection method based on a dual-channel multi-scale attention mechanism is proposed to detect low-level edges, boundaries, and target contours. First, a dual-channel backbone network was constructed, including a semantic branch channel and a spatial detail branch channel. Second, based on the multi-scale principle, a multi-scale and attention mechanism was built to transform the dimensions of the feature map's height, width, and channel, allocate feature weights, capture cross-channel information, and capture direction and position information. Finally, the hole fusion module was integrated into the semantic branch channel to improve the network's ability to extract feature information. Experimental results show that the proposed algorithm improves the edge detection performance of photovoltaic panel images. Compared to HED、RCF and FCN algorithms, the F1 value was increased by 2.83%、0.37% and 1.54%, respectively, and clearer crack images were obtained.