A lightweight multi-feature fusion network for unmanned aerial vehicle infrared ray image object detection

作者：Yunlei Chen, Ziyan Liu, Lihui Zhang, Yingyu Wu, Qian Zhang, Xuhui Zheng · 发表于：The Egyptian Journal of Remote Sensing and Space Science · 年份：2024 · DOI：10.1016/j.ejrs.2024.03.001 · 被引用次数：10 · 研究领域：Infrared Target Detection Methodologies、Advanced Neural Network Applications、Video Surveillance and Tracking Methods

In light of issues such as unnoticeable texture features and limited resolution of infrared image objects, a lightweight multi-scale feature fusion method for UAV infrared object recognition is presented to enhance the performance of UAVs carrying intelligent devices to detect infrared objects. By changing the anchorless frame strategy of the YOLOX method, a lightweight Multi-Feature Fusion Network (MFFNet) for UAV IR image object recognition is proposed. First, a lightweight backbone network is built using ShuffleNetv2_block, spatial pyramid pooling, and other modules to reduce the network's number of parameters and inference time while maintaining its capacity to extract features. Second, we develop a multi-feature fusion module to improve the detection capabilities of the model for IR objects by fusing the local features and the overall characteristics of IR objects since the texture features of IR objects are challenging to employ, but the boundary information is evident. The boundary frame regression loss is then optimized using SIoU by comparing the predicted frame to the actual frame in terms of angle, distance, shape, and IoU (Intersection over Union), which forces the model to reach the optimum predicted box more quickly.