As technology advances, autonomous driving systems are gradually maturing. However, in complex outdoor conditions such as heavy rain or dense fog, these systems often face challenges due to low visibility, high noise levels, and loss of detail. To address these issues, this paper introduces an improved method for infrared target detection based on YOLOv8n. Initially, a FasterNet Block module is incorporated to enhance computational speed and the efficiency of spatial feature extraction. Subsequently, the MACSP module was introduced in the network’s neck. This module enhances the efficiency of feature extraction from images, enabling the capture and effective utilization of multi-scale features. Furthermore, enhancements to the detection head through the WiseIoU loss function have improved target localization accuracy. To demonstrate the effectiveness and versatility of the proposed method, experiments were conducted on the updated FLIR \((\text {FLIR\_ADAS\_v2})\) infrared dataset and the RTTS fog dataset. The experimental results reveal that the FMA-YOLO algorithm achieves an increase in mean Average Precision (mAP) by 2.0% on the FLIR dataset and 1.8% on the RTTS dataset, compared to the original YOLOv8n. The infrared detection approach presented in this paper significantly enhances target detection performance in complex scenarios, showcasing excellent performance and robustness, and offers a new perspective on infrared target detection technology.