An improved YOLOv8s model based on sphere convolution for object detection in fisheye images

At present, with the continuous development of computer vision technology, people's requirements for images are no longer limited to the degree of visual clarity, but pay more attention to the comprehensiveness of image information. Compared with the limited field of view of the traditional perspective camera, the fisheye camera has a blind-free field of view and can obtain rich visual information. However, the wide field of view of the fisheye camera causes large-scale variation and distortion problems, which creates a great challenge to the feature extraction of traditional object detection algorithms. According to the characteristics of fisheye images, we adopt the current state-of-the-art lightweight YOLOv8s model as the base model and introduce Sphere Convolution (SphereConv) to deal with the complex distortion characteristics of fisheye images. The proposed Sphere-YOLOv8s model effectively improves the distortion problem of fisheye images. The experimental results show that the Sphere-YOLOv8s model on the VOC-360 dataset obtains an excellent improvement in object detection compared to the original YOLOv8s model. Among them, Precision improves 12.5%, Recall improves 14.5%, mAP@0.5 improves 16.7%. mAP@0.5:0.95 improves 20.5%.