Convolutional Feature Frequency Adaptive Fusion Object Detection Network

While the convolutional layer deepens during the feature extraction process in deep learning networks, the performance of the object detection decreases associated with the gradual loss of feature integrity. In this paper, the convolutional feature frequency adaptive fusion object detection network is proposed to effectively compensate for the missing frequency information in the convolutional feature propagation. Two branches are used for high- and low-frequency-domain channel information to maintain the stability of feature delivery. The adaptive feature fusion network complements the advantages of missing high-frequency features, enhances the feature extraction integrity of convolutional neural networks, and improves network detection performance. The simulation tests showed that this algorithm's detection results are significantly enhanced on blurred objects, overlapping objects, and objects with low contrast between the object and background. The detection results on the Common Objects in Context dataset was more than 1% higher than the CornerNet algorithm. Thus, the proposed algorithm performs well for detecting pedestrians, vehicles, and other objects. Consequently, this algorithm is suitable for application in autonomous vehicle systems and smart robots.