LKC-Net: large kernel convolution object detection network
Weina Wang, Shuangyong Li, Jiapeng Shao, Huxidan Jumahong
Abstract
Deep learning-based object detection methods have achieved great performance improvement. However, since small kernel convolution has been widely used, the semantic feature is difficult to obtain due to the small receptive fields, and the key information cannot be highlighted, resulting in a series of problems such as wrong detection, missing detection, and repeated detection. To overcome these problems, we propose a large kernel convolution object detection network based on feature capture enhancement and vast receptive field attention, called LKC-Net. Firstly, a feature capture enhancement block based on large kernel convolution is proposed to improve the semantic feature capturing ability, and depth convolution is used to reduce the number of parameters. Then, the vast receptive filed attention mechanism is constructed to enhance channel direction information extraction ability, and it is more compatible with the proposed backbone than other existing attention mechanisms. Finally, the loss function is improved by introducing the SIoU, which can overcome the angle mismatch problem between the ground truth and prediction box. Experiments are conducted on Pascal VOC and MS COCO datasets for demonstrating the performance of LKC-Net.