This study proposes an improved detection algorithm, YOLOv7-FSE (YOLOv7 with FReLU-SiLU-EIOU enhancements), to address the challenges of low resolution and poor detection accuracy in infrared images of composite material defects in aircraft. These limitations make it difficult to accurately characterize defect features. The proposed algorithm introduces several key modifications to the original YOLOv7 architecture. First, the SiLU activation function is replaced with the funnel activation function FReLU to improve spatial sensitivity to defect features. Subsequently, space-to-depth convolution (SPD Convolution) is employed to improve the feature extraction process, thereby enhancing the algorithm's ability to characterize complex defect features in low resolution infrared images. Finally, the EIOU loss function is replaced by the CIOU loss function, and the boundary box recognition weights are optimized to generate higher quality anchor boxes, further improving overall detection performance. Comparison results demonstrate that YOLOv7-FSE outperforms traditional detection methods such as Faster RCNN and YOLOv3. Specifically, it achieves a mean average precision (mAP) improvement of 10.8% over Faster R-CNN and 10.1% over YOLOv3. Compared to the original YOLOv7, the precision (P) increases from 88.3% to 94.9%, while the mAP rises from 90.1% to 97.7%. The YOLOv7-FSE algorithm is well-suited for infrared detection of composite material defects on aircraft surfaces and holds significant potential for integration with embedded devices for rapid, on-site defect detection.