Microarmature Solder Surface Detection: An Adaptive Central Region Sample Selection Anchor Free Framework

Abstract

As a core procedure in an increasingly automated industrial environment, defect detection is important for producing micro armatures. However, there are still some difficulties in using general deep learning detection methods, the spatial feature loss problem for small targets of solder surface defects and the difficulty in tuning the reference of sample selection strategy. Therefore, this paper proposes an adaptive label assignment detection framework. The detector uses an anchor free object detection network as the backbone. A feature compensation method is proposed to complement the spatial information for solving the feature disappearance problem of small objects. For addressing the label matching tuning problem of anchor-free network, an adaptive central region sample selection strategy is proposed at the base of the feature selective anchor-free (FSAF) module. To further improve the performance of the detection head, the dynamic head and soft non-maximum suppression (Soft-NMS) are introduced. The detection framework in this paper is evaluated on the Micro-Armature Solder Surface Defect Detection (MASS-DET) dataset, and the experimental results show that our framework can achieve 87.7 % mAP50 and 65.5% mAR, making it superior to common object detection methods. Moreover, the method presented in this paper can be applied in most industrial environments to improve the accuracy and efficiency of defect detection of non-standard micro workpiece. The dataset of this paper is open source and available on https://github.com/scuzw/Micro-Armature-Solder-Surface-Defect-Detection.