Underwater Object Detection Algorithm Based on Improved YOLOv7
Underwater object detection has become one of the hot research areas in computer vision.However,existing underwater scene datasets have issues such as a wide range of scale distributions and dense distribution of underwater targets.Addressing the limitations of existing optical imaging un-derwater object detection technologies in mining and characterizing multi-scale features of underwater objects,a YOLOv7 underwater object detection algorithm based on a scale-sequential feature pyramid and a pyramid partition attention mechanism.Firstly,a new scale-sequential feature pyramid is intro-duced on the basis of YOLOv7 to enhance the model's multi-scale feature extraction capabilities.Then,an efficient pyramid partition attention mechanism is introduced to improve the model's ability of extracting more fine-grained multi-scale spatial information.Finally,experiments conducted on two public datasets are carried out.Experiment result shows that the algorithm performs well on various underwater targets across different water bodies.
NETL
NSTL
万方数据
维普
