Infrared object detection network compression using Lp normalized weight

In view of the characteristic that the infrared image has less texture compared with RGB image, an infrared object detection network compression method using Lp normalized weight was proposed. It aimed at improving the adaptability of convolutional neural network based object detection framework to the infrared images, and compressing the scale of network while improving its generalization ability. Firstly, the phenomenon that the sparsity of Lp normalized weight can be precisely controlled by adjusting p was revealed. Based on the phenomenon, a sparsification method for object detection network was proposed. It respectively trained the backbone network and the detector with Lp spherical gradient descent and classical gradient descent, to balance the network scale and fitting accuracy. The tests on simulated infrared image dataset show that, the proposed method is superior to the dense model on both of network scale and detection accuracy: in terms of network scale, the sparsification reduces the effective parameters of Faster R-CNN, Single Shot multibox Detector (SSD) and YOLOv3 by 52%, 78% and 66% respectively; it also improves the mean Average Precision (mAP) of Faster R-CNN, SSD and YOLOv3 by 0.1%, 0.3% and 0.2%, thus verifying the effectiveness of the proposed method.