A Lightweight UAV Visual Obstacle Avoidance Algorithm Based on Improved YOLOv8

The importance of unmanned aerial vehicle (UAV) obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance, thereby protecting people and property. We propose UAD-YOLOv8, a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoid...

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Vydáno v:Computers, materials & continua Ročník 81; číslo 2; s. 2607 - 2627
Hlavní autoři: Du, Zongdong, Feng, Xuefeng, Li, Feng, Xian, Qinglong, Jia, Zhenhong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Henderson Tech Science Press 2024
Témata:
Adaptive algorithms
Algorithms
Collision avoidance
Convolution
Flight safety
Formability
Obstacle avoidance
Parameters
Unmanned aerial vehicles
ISSN:1546-2226, 1546-2218, 1546-2226
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Shrnutí:The importance of unmanned aerial vehicle (UAV) obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance, thereby protecting people and property. We propose UAD-YOLOv8, a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance. The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2 (DCNv2) to optimize the cross stage partial bottleneck with 2 convolutions and fusion (C2f) module. Additionally, it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution (UGDConv) series of lightweight convolutions and a lightweight detection head. Based on this, we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different environments. In particular, we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance capability. To verify the effectiveness of the algorithm, the UAV obstacle detection (UAD) dataset was created. The experimental results show that UAD-YOLOv8 improves mAP50 by 3.4% and reduces GFLOPs by 34.5% compared to YOLOv8n while reducing the number of parameters by 77.4% and the model size by 73%. These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments, demonstrating its wide range of applications.
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ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2024.056616