| Citation: | MENG W J，AN W，MA S G，et al. An object detection algorithm based on feature enhancement and adaptive threshold non-maximum suppression[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2349-2359 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0534
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To further solve the problems of object omission and repeated detection and improve the accuracy of object detection, this paper proposes an object detection algorithm based on feature enhancement and adaptive threshold non-maximum suppression(NMS). The attention-guided multi-scale context module(AMCM) is applied to the neck of the detector. Based on improving the semantic information of features by dilated convolution, the cross-channel location information is captured by the attention mechanism, so as to enhance the feature expression ability of the network. The dynamic suppression threshold is adaptively applied to instances of the scenes through the adaptive density threshold of NMS（ADT-NMS）, which lowers the false detection rate for objects. In comparison to the baseline algorithm YOLOv4, the suggested approach’s false detection rate on the PASCAL VOC dataset is 13.7%, a 1% decrease. The recall rate and detection accuracy increase by 0.9% and 1.7%, respectively, to 96.6% and 83.7%. The false detection rate of the proposed algorithm on the KITTI dataset achieves 22.1%, reduced by 1.3%. The detection accuracy and recall rate achieved 83.6% and 91.8%, improved by 1.8% and 2.3%, respectively. The experimental results show that the algorithm can better solve the problems of object omission and repeated detection.
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