Multi-YOLOv8: An infrared moving small object detection model based on YOLOv8 for air vehicle

The detection of infrared moving small objects faces significant challenges in the field of object detection for air vehicles. These types of objects usually occupy a small number of pixels in an infrared image, resulting in limited feature information, considerable feature loss, low recognition accuracy, and various challenges in single-frame detection. To address these challenges, this paper proposes an efficient multi-input method named Multi-YOLOv8, which is based on the YOLOv8s model. The proposed method uses current frames as a primary input and incorporates optical flow processing images and background suppression images as auxiliary inputs to improve detection performance. In addition, an improved method is developed for optical flow computations, named the pyramidal weight-momentum Horn–Schunck (PWMHS) method, which can process optical flows efficiently and precisely. An improved version of the Wise-IoU (WIoU) v3, referred to as α⁎-WIoU v3, is proposed as a bounding box regression (BBR) loss function to optimize the YOLOv8 network. Further, the BiFormer module and lightweight convolution GSConv are introduced to improve the attention to key information for the objects and balance the computational cost and detection performance, respectively. Moreover, a small object detection layer is added the YOLOv8 network to improve the capability for small object detection. Finally, a warming-up training method that can reduce the dependency on auxiliary inputs and ensure model stability in case of auxiliary input failures is developed. The results of the comprehensive experiments on an open-access dataset reveal that the proposed model outperforms the mainstream models in overall performance. The proposed method can significantly enhance the detection ability of infrared moving small objects.