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

UAV (Unmanned Aerial Vehicle) infrared object detection is crucial in pedestrian monitoring and traffic dispatch, which detects and locates objects in infrared images. 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 detection 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 MultiFeature Fusion Network (MFFNet) for UAV infrared ray (IR) image object detection 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 SCYLLA-IoU (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. The experimental results demonstrate that our method achieves an 81.5% mean average precision (mAP) with 4.21M parameters and an inference time of only 4.84ms per image, outperforming most networks in speed and accuracy.