A pedestrian detection method based on YoloV5 RGB and thermal image data fusion is proposed in this paper. This method uses two yolov5 branch structures to learn the features of RGB and thermal image data respectively, and finally uses multimodal features for fusion detection. It includes the following stages. Firstly, we use yolov5 network as a branch network to learn features from paired RGB and thermal image data. The two yoloV5-based backbone networks extract the features of the two modes for preliminary fusion, and then obtain the importance parameters of the two modes through feature compression and extraction. The effective information is enhanced, and redundant information is eliminated by multiplying the initial features. Finally, the fusion feature is used for target detection. Through this method, the detection effect is improved. We have done a lot of experiments on the public KAIST and VOT2019 pedestrian data set, and the experiments show that our method is better than the advanced method.
This paper presents a novel CNN model called comparison prediction network for apparent age estimation. The algorithm is structured by feature extraction and Face feature database. Compared with the existing methods, our algorithm can better deal with the problem caused by differences between apparent age and actual age, which improves the prediction precision of the model with the increasing credibility and robustness of the model prediction results, enhancing the generalization ability of the model. The algorithm has fewer parameters and is lighter than other methods, which is suitable for mobile deployment.
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