Object Detection Algorithm Based on Channel Separation Dual Attention Mechanism

Abstract

For the problems of low detection accuracy and high leakage rate of small targets in two-stage object detection algorithm, a target detection algorithm based on channel separation and dual attention mechanism is proposed to improve the detection accuracy of small targets by improving the Faster+FPN backbone network. Firstly, in response to the problem that neural networks can not automatically learn the importance between features, a dual-attention mechanism is proposed to build a deep neural network in the channel separation process, and other techniques such as group convolution and dilated convolution are combined to reduce network parameters. Secondly, to address the problem of information loss caused by high resolution features passing through a deep CNN, the detail extraction module and channel attention feature fusion module are added to extract more detailed features. Finally, considering that the general loss function cannot be focused on assessing the confidence level of the target’s location, the KL scatter is combined with the loss function optimization to make the prediction distribution closer to the real distribution through training, and the problems associated with the direct use of neural networks for object detection are effectively addressed. PASCAL VOC2007, KITTI and Pedestrian datasets are adopted to train the network, and the proposed model is compared with several object detection algorithms. Experimental results show that the proposed algorithm in this paper can recognize images efficiently and has high detection accuracy.

Keywords

• channel separation; dual attention mechanism; feature pyramid networks (fpn); kl divergence; object detection