Abstract: Injection empty bottles can produce a large number of defects on the surface of bottles during production, and these defects have a significant impact on the appearance and use of the product. The traditional manual detection is no longer applicable due to the shortcomings of high labor intensity and low detection efficiency. For the traditional scene detection algorithms based on machine vision, the extracted features are often difficult to be used for defect classification and recognition for complex scene changes. Therefore, this paper proposes an SSD-based algorithm to detect the surface defects of injection empty bottles. Considering that the surface defect of empty bottle is small, it is difficult to extract features. In order to improve the detection effect, a feature fusion module is added to the SSD network structure to provide rich semantic features for the prediction layer. At the same time, an attention mechanism is introduced in the network to increase the feature extraction capability of the network and effectively improve the detection accuracy of the network. The method of this paper is verified on the empty bottle surface defect data set. The accuracy rate is 98.3%, the missed detection rate is 0.74%, the false detection rate is 0.96%, and the mAP is 96.5%. Compared with the mAP of original SSD algorithm, the algorithm in this paper improves by nearly 5.6 percentage points.

Key words: defect detection, convolutional neural network, attention mechanism, scale feature fusion module, global context block