Detection of small rod-end joint bearings via deep feature fusion and confidence propagation clustering

Abstract

Machine vision is used to detect dense, small rod-end joint bearings in sliding ball surfaces with little feature information and high variability. However, this leads to inaccurate identification, affecting production efficiency. This study proposes a deeplearning object-detection algorithm model that allows the network to retain more semantic information. We introduced the space-to-depth convolution (SPD-Conv) step-free convolution module to improve the backbone network and developed a multi-level feature fused SPD (MFSPD) deep feature fusion module to redesign the neck network to improve the feature extraction ability and detection accuracy for small targets. Furthermore, we added a small P4 detection head in the head network (i.e., prior box acquisition on the dataset using the weighted k-means algorithm), increased the matching degree of the prior box and feature layer, and accelerated the model convergence. To improve the confidence propagation clustering (CP-Cluster) analysis algorithm for post-processing, we optimized the prediction box confidence degree and detection speed. The algorithm performance was evaluated on homemade, T-LESS, and COCO datasets. The mAP@.5 values of the target detection algorithm for the homemade and T-LESS datasets were 96.9% and 93.8%, respectively, and the mAP was 55.9% for the COCO dataset. The experimental results indicate that the algorithm has a high detection accuracy and good feature extraction ability. Thus, it has considerable advantages for small-object detection and provides a reference for the detection of small parts.

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