Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes, an improved you only look once version 8 (YOLOv8) object detection algorithm for infrared images, F-YOLOv8, is proposed. First, a spatial-to-depth network replaces the traditional backbone network’s strided convolution or pooling layer. At the same time, it combines with the channel attention mechanism so that the neural network focuses on the channels with large weight values to better extract low-resolution image feature information; then an improved feature pyramid network of lightweight bidirectional feature pyramid network (L-BiFPN) is proposed, which can efficiently fuse features of different scales. In addition, a loss function of insertion of union based on the minimum point distance (MPDIoU) is introduced for bounding box regression, which obtains faster convergence speed and more accurate regression results. Experimental results on the FLIR dataset show that the improved algorithm can accurately detect infrared road targets in real time with 3% and 2.2% enhancement in mean average precision at 50% IoU (mAP50) and mean average precision at 50%—95% IoU (mAP50-95), respectively, and 38.1%, 37.3% and 16.9% reduction in the number of model parameters, the model weight, and floating-point operations per second (FLOPs), respectively. To further demonstrate the detection capability of the improved algorithm, it is tested on the public dataset PASCAL VOC, and the results show that F-YOLO has excellent generalized detection performance.