An enhanced method for surface defect detection in workpieces based on improved MobileNetV2-SSD

In the process of workpieces production, surface defects are prone to occur, and these defects come in a wide variety and are often intermixed, making defect detection and classification exceptionally challenging. With the development of artificial intelligence and deep learning, to tackle this problem, this paper introduces an enhanced single shot multibox detector algorithm based on MobileNetV2 for the detection of surface defects. The method utilizes MobileNetv2 as the backbone of the feature extraction network to obtain six feature layers with different detection scales from the baseline network, that is, the original 1 x 1 feature prediction layer is deleted and a 75 x 75 feature prediction layer is added, which is closer to the specific features of the defects on the surface of the workpiece. In the additional feature layer, two parallel dilated convolution structures are connected, introducing depth-separable convolution and dilated convolution, combining skip connections and pixel-wise addition operations. A special feature fusion structure is proposed to perform feature fusion for small, medium and large target detection layers, which effectively solves the problem of missed and false detection. Moreover, it refines candidate bounding box aspect ratios within the training set through the utilization of the K-means clustering algorithm, ensuring a better match with real boxes. The experimental results demonstrate the effectiveness of the enhanced model, and the mean average precision value reaches 88.72%. Compared to other state-of-the-art detection methods, it exhibits superior capabilities.