Laser & Optoelectronics Progress, Volume. 62, Issue 10, 1037009(2025)
Unmanned Aerial Vehicle Small Target Detection Algorithm Based on YOLOv8n
To address the challenges of insufficient feature information in small targets within unmanned aerial vehicle (UAV) aerial images and significant variation in target scales, a small target detection algorithm based on an improved YOLOv8n is proposed. The improvements focus on the following aspects: i) design of a multi-scale feature fusion layer in the detection layer tailored for feature extraction and processing of small targets to improve the ability to collect detailed information of small targets; ii) concerning the neck network, a triple-feature fusion module and a scale sequence feature fusion module are introduced to effectively fuse the detailed information from low-level feature maps with the semantic information from high-level feature maps. This enhances detection capabilities across targets of varying scales. In the backbone network, ConvNeXt v2 is used to replace the backbone network, thereby enhancing the localization and feature extraction capabilities of small targets against complex backgrounds. To optimize the deployment of the model in embedded systems, a layer adaptive amplitude pruning algorithm is adopted to balance the computational complexity and detection accuracy of the model. The algorithm performance is tested using the VisDrone2019 dataset and compared with several mainstream models. The experimental results indicate that the improved algorithm attains a detection accuracy (mAP50) of 30.2%, which is 3.8 percent point higher than that of the benchmark model, YOLOv8n, while reducing the parameter count by 1.63×106.
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Lizhi Zhu, Hui Wei. Unmanned Aerial Vehicle Small Target Detection Algorithm Based on YOLOv8n[J]. Laser & Optoelectronics Progress, 2025, 62(10): 1037009
Category: Digital Image Processing
Received: Sep. 24, 2024
Accepted: Dec. 2, 2024
Published Online: Apr. 28, 2025
The Author Email: Hui Wei (89192138@qq.com)
CSTR:32186.14.LOP242022