Abstract: Aiming at the problems of apple defect detection， such as frequent false detection， leakage detection and easy confusion of defects， we propose an apple defect detection algorithm based on improved YOLOv5. Apple defect detection is very important for apple sorting. The existing methods of apple defect detection mainly extract color and texture features through machine learning or convolutional neural network， but there are problems such as error detection， missing detection and insufficient feature extraction ability. It can not meet the requirements of accuracy and real-time defect detection. NAM-YOLO algorithm mainly has three core ideas: 1） By adding TRANS module to the backbone network， features and global information can be better integrated； 2） The weighted bidirectional feature pyramid network is used to fuse features of different scales； 3） The NAM attention mechanism based on normalization is introduced into the neck network to strengthen the key features of the target region and improve the detection accuracy of the network. Experimental results show that the mAP of the improved algorithm reaches 98.90% and the accuracy is 98.73%. Compared with other models， this model has better feature fusion ability and can better meet the actual needs of apple sorting.

Key words: Key words: NAM-YOLO, YOLOv5, TRANS, attention mechanism, defect detection