基于 YOLOv5 的无人车自主目标识别优化算法

Autonomous target recognition is a key technology for enabling intelligent operation of unmanned vehicles in complex ground battlefield environments. However, deploying and applyingdeep learning-based autonomous target recognition algorithmson resource-constrained embedded computing platformsfor realizing intelligent unmanned vehicles remains a challenging task. Based on the YOLOv5 deep neural network structure used for autonomous recognition of ground targets, this paper proposes a multi regularization adaptive network pruning algorithm based on animproved attention module and BatchNorm layer. The proposed algorithm achieves optimal pruning of the network structure during the collaborative process of pruning and training. A combined posttraining INT8 quantization algorithm is designed, employing unsaturated mapping for weights and saturated mapping for activation values. The compressed and optimized YOLOv5x network is then deployed and verified on the embedded computing platform based on the Zynq UltraScale + MPSoC architecture. The verification results show that when YOLOv5x network prunes 40% of the channel and quantizes with INT8 strategy, the recognition accuracy for infrared dataset is only reduced by 0. 374% . The recognition accuracy for visible light dataset is increased by 0. 065%, and the target recognition frame rate can reach 25 frames per second. The optimized network can meet the accuracy and real-time requirements of autonomous target recognition in the complex battlefield environment of unmanned vehicles. The proposed network optimization algorithm can be extended to other combat platforms, such as unmanned aerial vehiclesand precision-guided weapons.