Feature Fusion Module Based on Gate Mechanism for Object Detection

In recent years, deep learning based feature fusion has drawn significant attention in the field of information integration due to its robust representational and generative capabilities. However, existing methods struggle to effectively preserve essential information. To this end, this paper proposes a gate-based fusion module for object detection to integrate the information from distinct feature layers of convolutional neural networks. The gate structure of the fusion module adaptively selects features from neighboring layers, storing valuable information in memory units and passing it to the subsequent layer. This approach facilitates the fusion of high-level semantic and low-level detailed features. Experimental validation is conducted on the public Pascal VOC dataset. Experiments results demonstrate that the addition of the gate-based fusion module to the detection task leads to an average accuracy increment of up to 5%.