Abstract:In the field of infrared object detection, the accuracy of deep learning algorithm based on convolutional neural network has exceeded that of traditional pattern recognition algorithms. However, the implementation of neural networks requires huge computing and storage demands, and it is difficult to deploy them on embedded platforms such as drones. To overcome the above problem, this paper introduces a channel-wise quantization strategy and the approximate optimization of the gradient into the training of low-bit neural network model. Besides, a hardware computing resources utilized FPGA accelerator is proposed to implement the model, whose overall performance is 65.6 GOPS. Comparisons with other related works show that the low-bit quantization method and the proposed FPGA accelerator implementation can provide a solution with high energy efficiency and high recognition accuracy for the embedded infrared object detection system.