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Multihigh order target detection method based on YOLOv3 model
- YAN Chen-xu, SHAO Hai-jian, DENG Xing
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JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2022, 57(3):
20-30.
doi:10.6040/j.issn.1671-9352.4.2021.034
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Target detection is an important branch of computer vision, although the current target detection approaches based on deep learning can solve the issues that are usually caused by traditional target detection methods in detection accuracy and detection time, it is still difficult to take both detection speed and detection accuracy into account. Therefore, this paper proposes the Mul-YOLO target detection network based on the improved YOLOv3, which uses Haar wavelet for data preprocessing, decomposes low-frequency features of image information layer by layer in different resolutions, and then obtains high-frequency features in horizontal, vertical and diagonal directions. The information recorded by the aforementioned high-frequency features can reduce the negative effects to detection accuracy that are usually brought by geometric state change, illumination change and background change. Convolution and concatenating on the feature layer in combination with the third-order calculation are integrated, and the feature extraction which makes the training network pay more attention to the significant information of the mapping features, is strengthened in the limited receptive field. This enhances the image resolution, and makes up for the problem of information loss caused by continuous convolution and pooling in the data set training process. The experimental results on PASCAL VOC data sets show that the proposed Mul-YOLO target detection approach has obvious improvements compared with the previous generation of target detection model. For example, mAP is improved by 8.97%compared with the Faster R-CNN ResNet feature extraction method, the detection time of single image is decreased by 172 ms, while mAP is increased by 30.48% compared with the YOLOv3 feature extraction method, achieveing the purpose that detection accuracy and detection time complement each other at the same time. The detection accuracy is therefore improved, and the detection time remains unchanged and the effectiveness of proposed approaches can be guaranteed also.
