To address the problems of large defect size span, similar characteristics, difficulty in recognition of small targets, and missed objects in chip defect detection, an improved method based on YOLOv5 is proposed. To solve missed and false detection of small targets, we presented a new small target feature detector ( S-Detector) to improve the learning capability of the model. For the large defect size span and similar characteristics, efficient attention feature pyramid networks (EA-FPNs) with highly active focus learning ability are proposed to improve the ability to detect different sizes of defects. The bounding box fusion algorithm (BFA) is developed to reduce the redundant boxes and time overhead in prediction. The experimental results show that the detection accuracy of this method is enhanced by 1. 2% and the accuracy of minor target defects is improved by 1. 6%; while using BFA to eliminate the redundant boxes, the detection time of a single image is 26. 8 μs, which is decreased by 5. 2 μs before BFA. The proposed method has good performance and efficiency in chip defect detection. Keywords:chip defect detection; deep l