基于改进YOLOv3和核相关滤波算法的旋转弹目标探测算法

The image captured by the spinning projectile-borne TV camerawill rotate and jitter to become blurry. It is difficult to detect a target accurately when the target data is less in advance detection and the field of view in the terminal guidance phase is small. A target detection and tracking algorithm based on improved YOLOv3 and kernelized correlation filter (KCF) is proposed. On the premise of a small number of data samples, the complex environments such as different weather, illumination, motion, and rotation blur are simulated to complete the data enhancement and expansion in network learning; By adding the multi-scale branch structure of Induction based on YOLOv3 network, the adaptability of the network to different sizes of targets is increased and the number of network layers is reduced for small target detection. In the realization of target location method, the target detection is combined with tracking algorithm, a target loss discrimination mechanism based on Gaussian threshold is proposed, and the target frame scale is updated by using the velocity-time information of trajectory. Simulated results show that the improved algorithm can achieve the target detection and tracking in the complex environment more effectively.