YOFIR: High precise infrared object detection algorithm based on YOLO and FasterNet

Infrared images often suffer from issues such as blurriness and unclear object boundaries, and existing object detection algorithms are developed based on visible light images, which makes infrared object detection more challenging. Therefore, this paper proposes an infrared image enhancement method and an infrared object detection algorithm based on YOLO and FasterNet, named YOFIR. Specifically, we apply CHALE, Auto Gamma, histogram equalization, and bilateral filtering to process images individually, then fuse the results with different weights to address the poor imaging quality of infrared images. Moreover, we utilize the FasterNet network for multi-scale feature extraction to adapt to low-resolution infrared images. We also reduce model parameters through GSConv and propose a novel Efficient Multi-Scale Group Convolution module, EMSGC, which enhances feature fusion by processing feature maps from different channels, effectively improving detection accuracy. Finally, the DyHead Block is incorporated into the head to enhance the capability of infrared object detection. Experimental results on the HIT-UAV infrared remote sensing dataset show that the proposed algorithm achieves a 4% improvement in $mA{P}_{0.5}$ compared to YOLOv8. Moreover, on the FLIR dataset, the algorithm shows a 1.6% improvement in $mA{P}_{0.95}$ over YOLOv8, with significant advantages in terms of model parameters and FLOPs.