Aiming at the constraints of computing power and storage resources of embedded devices, this paper puts forward an improved model lightweight scheme, mainly introducing model distillation and model quantization methods. After L2 loss knowledge distillation experiment based on intermediate features, the accuracy of YOLOv5s model is improved by 1.6%. Through asymmetric quantization experiment of PTQ INT8, the speed is improved by nearly 2.6 times when the model accuracy is only 2.3%, which meets the demand of real-time reasoning. After the quantitative perceptual training, the speed of the model is increased by 1.8 times with a loss of only 1.4%. The improved lightweight model is deployed in embedded devices, and its performance is tested. Under the condition that the accuracy of the original model is 97% on RK3588S, the reasoning speed is increased by 2.6 times.
An artificial intelligent decision-making system based on Deep Q Network is developed according to the characteristic of the optoelectronic countermeasures for defense. In view of the high complexity of the input state variables of the system, simulation method is used to sift the state variables so as to reduce the input dimension of the network. In addition, simulation method is used to generate enough samples for the network training. Aiming at the adaptability evaluation of the system, evolutionary evaluation index is designed and simulation method is used to evaluate the adaptability of online learning ability of the system.
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