Abstract: In deep networks,the feature information of the input data is gradually abstracted and compressed during the feed-forward process,resulting in some of the feature information that is crucial for object detection being diluted or lost.Based on YOLOv11n,an object detection method with deep feature enhancement and path aggregation optimization is proposed. Firstly,GLFEM is designed to combine the local features of the feature map with the global features to strengthen the expression ability of the deep network features.Then,AFEM is designed to dynamically enhance the feature extraction ability of the deep network according to the reliability of the features. Finally,the path aggregation feature pyramid network is optimized to fuse the feature information between different levels and reduce the semantic information difference between levels.Experimental results on three public datasets,VisDrone,NWPU VHR-10,and TinyPerson,show that the average detection accuracy of the proposed method is improved compared to current state-of-the-art object detectors.Experiments on the self-built dataset AirportTiny also show the proposed method achieves good performance,it has good generalisation ability.

Key words: Object detection, Deep network, Path aggregation, Feature information, Feature enhancement