Research on Multi-Scene Electronic Component Detection Algorithm with Anchor Assignment Based on K-Means

Achieving multi-scene electronic component detection is the key to automatic electronic component assembly. The study of a deep-learning-based multi-scene electronic component object detection method is an important research focus. There are many anchors in the current object detection methods, which often leads to extremely unbalanced positive and negative samples during training and requires manual adjustment of thresholds to divide positive and negative samples. Besides, the existing methods often bring a complex model with many parameters and large computation complexity. To meet these issues, a new method was proposed for the detection of electronic components in multiple scenes. Firstly, a new dataset was constructed to describe the multi-scene electronic component scene. Secondly, a K-Means-based two-stage adaptive division strategy was used to solve the imbalance of positive and negative samples. Thirdly, the EfficientNetV2 was selected as the backbone feature extraction network to make the method simpler and more efficient. Finally, the proposed algorithm was evaluated on both the public dataset and the constructed multi-scene electronic component dataset. The performance was outstanding compared to the current mainstream object detection algorithms, and the proposed method achieved the highest mAP (83.20% and 98.59%), lower FLOPs (44.26GMAC) and smaller Params (29.3 M).