Abstract
With the increasing application of visual application scenarios in various industries, power grid inspection system based on vision is widely used. However, the power grid intelligent inspection system has the problem of unsatisfactory accuracy of small target detection. To address this problem, this paper proposes a multi-scale fusion-based defect detection model for grid inspection images. The object features are first acquired by improving the CSPDarknet53 network using the attention mechanism. Second, the multi-scale feature fusion module is constructed to obtain more feature information. Finally, the defect detection model of the grid inspection image is constructed based on the multi-scale feature fusion module. The experimental results of our proposed algorithm are compared and analyzed with the current mainstream algorithms in all aspects. The optimal performance accuracy of our model reaches 0.927, which is improved by 6.06%.
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