Prof. Zhonghua Han Profile

Prof. Zhonghua Han

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Proceedings Article | 3 February 2023 Paper

Aerial image detection of drone based on improved YOLOv5

Zhijun Gao, Zhi Yang, Zhonghua Han

Proceedings Volume 12511, 125111J (2023) https://doi.org/10.1117/12.2660017

KEYWORDS: Target detection, Head, Transformers, Sensors, Neck, Data modeling, Image quality, Detection and tracking algorithms, Convolution, Visualization

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Drones have been used in many practical applications,,such as agriculture, aerial photography and surveillance. Therefore, it is very difficult for machines to automatically understand the visual data collected by drones, which makes the connection between computer vision and drones become inseparable. However, the identification of targets from aerial images is faced with the difficulty that the target to be detected is usually too small, too dense, and the relative size is not fixed relative to the image, and the relative motion of drone and traffic leads to the blurred target. To address these challenges, this paper proposes an improved YOLOv5 network model. Based on YOLOv5, we integrate the convolution block attention model ( CBAM ) and the attention mechanism of Swin Transformer to enable it to effectively focus on the attention area in dense small object scene and reduce the calculation amount. We also adopt the BiFPN structure for the structure of the neck network, which can obtain more effective feature information and reduce some unnecessary connections. In addition, we also add an additional detector to detect small objects. To further improve our YOLOv5, we adopt the data enhancement strategy. Extensive experiments on the VisDrone 2019 dataset show that the The AP result of the improved YOLOv5 was 32.83 %. Compared with the baseline model ( YOLOv5 ), YOLOv5 increased by about 6.5 %, indicating that the improved model was effective.

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