For the routine target detection algorithm in the underwater complex environment to obtain the image of the existence of blurred images, complex background and other phenomena, leading to difficulties in model feature extraction, target miss detection and other problems. Meanwhile, an improved YOLOv7 model is proposed in order to improve the accuracy and real-time performance of the underwater target detection model. The improved model is based on the single-stage target detection model YOLOv7, incorporating the CBAM attention mechanism in the model, so that the feature information of the detection target is weighted and enhanced in the spatial dimension and the channel dimension, capturing the local relevance of feature information, making the model more focused on target feature information, improved detection accuracy, and using the SPPFCSPC module, reducing the computational effort of the model while keeping the model perceptual field unchanged, improved inference speed of the model. After a large number of comparison experiments and ablation experiments, it is proved that our proposed ACFP-YOLO algorithm model has higher detection accuracy compared with Efficientdet, Faster-RCNN, SSD, YOLOv3, YOLOv4, YOLOv5 models and the latest YOLOv7 model, and is more accurate for target detection tasks in complex underwater environments advantages.

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High quality underwater images and videos are important for exploitation tasks in the underwater environment, but the complexity of the underwater imaging environment makes the quality of the acquired underwater images generally low. To correct the chromatic aberration and enhance the sharpness of underwater images in order to improve the quality of underwater images, we based on the differential compensation proposed a Differential Attenuation Compensation (DAC) method. The underwater image is contrast stretched to improve the contrast of the image, as well as the underwater image is denoised, for the red channel with serious loss of detail information we choose the blue and green channels with more detail information to compensate for this, and finally the image is restored through the grayscale world to obtain more realistic colors. Our method is qualitatively and quantitatively compared with multiple state-of-the-art methods in the public underwater image dataset, underwater image enhancement benchmark (UIEB) and enhancing underwater visual perception (EUVP), demonstrating that the underwater images processed by our method better resolve the problems of chromatic aberration and blur, with more realistic color, detail and better underwater image quality evaluation indicators