Supervisor:
Southwest Ordnance Industry Bureau
Organizer:
Chongqing Ordnance Industry Society
Chongqing University of Technology
Chongqing University of Technology
Detection algorithm of ground target based on attention and channel rearrangement for UAV
| DOI: | 10.11809/bqzbgcxb2024.03.040 |
| Keywords: | small object detection; deep learning; attention mechanism; channel shuffle; lightweight model |
| Abstract: | In the context of autonomous unmanned aerial vehicle (UAV) ground attacks, there are challenges such as time sensitive UAV combat, complex ground target recognition scenarios, slow model training and inference speeds, and missed and false detections of small targets. To address these challenges, a novel UAV to ground target detection algorithm based on attention mechanism and channel shuffle ideas is proposed. The algorithm incorporates the Coordinate Attention (CA) mechanism to enhance the network’s feature extraction capability for specific regions of interest. Additionally, the main network is subject to lightweight processing using channel shuffle to mitigate feature loss caused by multiple convolutions. Lastly, the algorithm replaces some activation functions with H Swish and optimizes the loss function as Complete Intersection over Union (CIoU) to improve the training and inference speed during wartime. Experimental results demonstrate that the proposed algorithm achieves a 28.4% improvement in training speed, a mean Average Precision (mAP) of 99.1% for target recognition, can detect targets as small as 19×25 pixels, and a detection rate of 72.99 FPS after acceleration using TensorRT, meeting the requirements for real time detection. Moreover, the algorithm shows strong performance in detecting small targets such as tanks in complex terrain. |
| Published: | 2024-03-28 |