Mr. Ji Gao Profile

Ji Gao

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Proceedings Article | 19 February 2024 Paper

Small target detection based on attention mechanism feature fusion

Ji Gao, Zipeng Zhang

Proceedings Volume 13063, 130630V (2024) https://doi.org/10.1117/12.3021360

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In recent years, the existing target detection algorithms have difficulties in detecting small targets, such as low target resolution, large interference in complex scenes, and lack of large and complete small target detection data sets. To solve the above problems, this paper proposes a new Yolov5 based target detection improvement model E-b-yolov5. This method first preprocesses the data, and then adds ECA to the last layer of Backbone structure by changing the basic network of Yolov5, This lightweight module effectively avoids dimension reduction. Secondly, CIoU is used as the loss function of frame regression to achieve high-precision positioning. In addition, Bifpn (Efficient Det) feature fusion is added to enhance the feature representation ability for small target detection, and the removal of single input side nodes reduces the amount of calculation. Finally, the model is distilled to increase the recall and accuracy of the model. Compared with the original yolov5 method, E-b-yolov5's mAP on the validation subset of the aerial photography dataset VisDrone 2019 DET reached 41%, 8% higher than YOLOv5's benchmark network; On the test subset, mAPreached33.4%, 4.3% higher than Faster R-CNN. research on small object detection to improve the detection effect of small object technology and extend its application scope in practical scenarios.

Proceedings Article | 19 October 2022 Paper

Binocular vision imaging algorithm under dynamic range compensation

Wufei Liu, Hua Li, Ji Gao, Xinlin Liu, Zipeng Zhang

Proceedings Volume 12294, 122944Q (2022) https://doi.org/10.1117/12.2639738

KEYWORDS: Cameras, High dynamic range imaging, Calibration, Image processing, Reconstruction algorithms, Image quality, 3D modeling, 3D image reconstruction, Image enhancement, Aberration correction

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Aiming at the problem of limited dynamic range in the imaging process of existing binocular cameras, a binocular vision algorithm based on HDRI (High Dynamic Range Imaging, HDRI) technology is proposed to improve image quality. It mainly includes the distortion correction processing of the low dynamic range image sequence collected by the camera; then the rapid calibration of the camera response function of the corrected image sequence to synthesize the high dynamic range image; and then the introduction of the chromaticity space color correction algorithm, in the image chromatic aberration and elimination Perform linear interpolation between chromatic aberrations to achieve the goal of self-adaptive correction of image chromaticity information. The experimental results show that the HDR (High Dynamic Range, HDR) image synthesized by the proposed algorithm has clear surface details and balanced colors, and has a good optimization effect in terms of imaging dynamic range compensation and image visualization, which further improves the image quality after binocular vision imaging and has good practical value.

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