The tidal movement of the ocean carries garbage to the shore. The garbage needs to be dealt with in time, otherwise, the pollution of the garbage to the environment will become increasingly serious. According to statistics, plastic garbage accounts for a substantial proportion of marine garbage. This study developed a target detection model for some plastic garbage to help achieve automatic marine garbage capture. Firstly, according to the principle of balanced label distribution, multi-background, and multi-angle, we created an image dataset based on artificial synthesis to solve the problem of insufficient data. Secondly, the CBAM attention module was used for the target detection algorithm Yolov5 to improve the ability of target feature extraction and model generalization. Furthermore, the loss function of bounding box regression CIoU was replaced with SIoU to solve the problems of slow convergence speed and low training efficiency. Finally, the effectiveness of the Yolov5 model was discussed with the analysis of experimental results.
The blood flow velocity in the nailfold capillary is an important indicator of the status of microcirculation. The conventional manual processing method is both laborious and prone to human artifacts. A feasible way to solve this problem is to use machine learning to assist in image processing and diagnosis. Inspired by the Two-Stream Convolutional Networks, this study proposes an optical flow-assisted two-stream network to segment nailfold blood vessels. Firstly, we use U-Net as the spatial flow network and the dense optical flow as the temporal stream. The results show that the optical flow information can effectively improve the integrity of the segmentation of blood vessels. The overall accuracy is 94.01 %, the Dice score is 0.8099, the IoU score is 0.6806, and the VOE score is 0.3194. Secondly, The flow velocity of the segmented blood vessel is determined by constructing the spatial-temporal (ST) image. The blood flow velocity evaluated is consistent with the typical blood flow speed reported. This study proposes a novel two-stream network for blood vessel segmentation of nailfold capillary images. Combined with ST image and line detection method, it provides an effective workflow for measuring the blood flow velocity of nailfold capillaries.
With the rapid development of marine resource exploitation, planktonic microorganisms have gradually become one of the research directions in the field of machine vision. In order to optimize the detection of small targets in the images of planktonic microorganisms, this paper proposes an improved YOLOv5s model to enhance the detection of planktonic microorganisms. The SE attention mechanism allows the network to pay more attention to the target feature area and suppress useless feature information. The PANet feature module is improved into a weighted bidirectional pyramidal BiFPN feature fusion network to achieve high-efficiency bidirectional cross-scale connectivity and weighted feature map fusion. The results show that the combination of the SE attention mechanism and BiFPN feature fusion improves the mAP value by 6.96%, increases the precision by 11.45%, and reduces the loss rate by 1.62%. Our proposed method effectively solves the problems of false detection, missed detection, and low detection accuracy of the existing models for small target detection.
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