Laser & Optoelectronics Progress, Volume. 59, Issue 8, 0810006(2022)
Lightweight Target Detection Algorithm for Small and Weak Drone Targets
To address the security risks associated with drone "abuse", aiming at the high complexity of the existing deep learning-based drone target detection algorithm, which results in lengthy model trainings, large computing resources, limited input image size, and slow detection speed, a lightweight level drone target detection (DTD-YOLOv4-tiny) algorithm is proposed. The proposed algorithm is based on YOLOv4-tiny, and we optimized the Anchor box using the K-means++ clustering algorithm, added the detection head of the 52×52 size feature map to expand the scope of the algorithm for small targets, and combined it with the ShuffleNetv2 lightweight backbone network, and the reorg_layer downsample and sub-pixel upsample methods were used to optimize the Backbone, Neck, and Head of the YOLOv4-tiny algorithm. Eventually, we obtained the DTD-YOLOv4-tiny with a model size of 1.4 MB and a floating-point calculation (GFLOPs) of 1.1, which is a lightweight detection technique. The experiments demonstrate that the DTD-YOLOv4-tiny detection model does not limit the image input size, while ensuring low computational resource occupation and high real-time detection. Simultaneously, the algorithm with reduced parameters can also maintain accuracy when facing the original large-scale image. When using 960×540 size image as input on the Drone-vs-Bird 2017 dataset, the average precision (AP) @50 of the proposed algorithm achieved 95%, and the detection speed on the RTX2060 graphics card attained 113 frame/s;when using 1920×1080 size image as input on the TIB-Net dataset, the AP@50 of the proposed algorithm achieved 85.1%, and the detection speed on the RTX2080Ti graphics card attained 119 frame/s.
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Rongqi Jiang, Zecong Ye, Yueping Peng, Guorong Xie, Heng Du. Lightweight Target Detection Algorithm for Small and Weak Drone Targets[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0810006
Category: Image Processing
Received: Mar. 16, 2021
Accepted: Apr. 27, 2021
Published Online: Apr. 11, 2022
The Author Email: Jiang Rongqi (jjqqjjqq163@163.com), Peng Yueping (percy001@163.com)