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This paper is focused on the field of computer vision in order to investigate the presentation properties of retinal blood vessels. Combining the structure of convolutional neural networks, activation functions, and common metrics in semantic segmentation, a dynamic network model for retinal vessel segmentation based on computer vision is constructed. The purpose of this paper is to discuss the results of retinal vascular segmentation based on computer vision. The image connection and alignment pattern selection process is also established to match retinal vessel images by computer vision. The performance of the dynamic network constructed here and the results of retinal vessel segmentation were then analyzed in three publicly available datasets, DRIVE (digital retinal images for vessel extraction), CHASE_DB1, and STARE (structured snalysis of the retinal. The ROC (retinopathy online challenge) curves on all three datasets exceeded 0.9, showing high performance, and the area under the PR curve exceeded 0.88. The accuracy of the results for retinal vessel segmentation was around 96%. Based on the semantic segmentation direction in the field of computer vision in this study, the dynamic network for retinal vessel segmentation can be well constructed. [ABSTRACT FROM AUTHOR]

To solve the problem that individual visual features could not be accurately extracted from low-light and high-noise pig face images in intensive farming, the optimal fitting curve parameters of image brightness enhancement were defined, and the Zero-DCE model was improved and Denoise-Net was introduced to achieve brightness enhancement and high-noise suppression of a single low-light pig face image. The experimental results show that, compared with EnlightGAN, Zero-DCE, Retinex, and SSE, the algorithm in this paper (DCE-Denoise-Net) has good results on image quality metrics such as information entropy, Brisque, NIQE, and PIQE in the absence of reference images. The image quality is improved. On the basis of improving the low visibility of low-light images, denoising was achieved. It is more suitable for low-light pig face image enhancement in a real breeding environment. [ABSTRACT FROM AUTHOR]

With the rapid growth of Internet video image information, there is a large amount of redundancy in image data. Use less data stream information to transfer the image or the amount of information contained in the image. Its purpose is to reduce the redundancy of images, so as to store them at low bit rate and reduce the data storage space. In the general image compression method, the hybrid coding framework is adopted. Each algorithm adopts a fixed algorithm mode through a specific design algorithm, without global optimization. Image compression is mainly divided into prediction, transformation, quantization, digital entropy coding and other steps. At present, there are many researches on super-resolution network based on deep learning technology. The main function is to reconstruct high-resolution image replace image magnification low-resolution images such as linear interpolation, which has a great performance improvement image resolution, noise reduction, deblurring and so on, but there is no effective way to use super-resolution network applications to improve quality of compression reconstructed image quality. This paper involves a new method that using image super-resolution residual learning network to improve quality of compression image, our method, the reduced image is encoded into a content stream and a transmission corresponding parameter is encoded into a model stream. Firstly, the original image is scaled down 1/2 size of source image, then encode the small image into content stream with the existing codec. Secondly, the residual learning super-resolution (SR) network is used for image filtering to scale up reconstructed image with decode image resizing method and boost the quality of edge feature extraction of image. Our results show that there is significant performance improvement of h265 in low resolution reconstructed image (bits-per-pixel less than 0.1). [ABSTRACT FROM AUTHOR]

Human posture detection is easily affected by the external environment, resulting in blurred results of limb feature extraction. In order to improve the accuracy and speed of human motion detection, this paper proposes a deep learning-based motion detection method in competitive sports training. The double parallel convolution network algorithm in the depth learning algorithm is used to process the collected action information, extract the body action features, and greatly reduce the operation scale; With the help of the theory of motion mechanics, the mechanical parameters in the motion process are calculated to eliminate outliers and reduce feature dimensions; With the help of motion inertial sensors and joint degrees of freedom, the limb motion detection results are obtained. The experimental results show that the average recognition rate of the method for different motion actions is 99.5%, and the average detection time is 148 ms, with good application performance. [ABSTRACT FROM AUTHOR]

The automation transformation of container lifting operations is one of the main technical issues in Rail-Truck intermodal transportation. To solve this problem, this paper analyzes the advantages and disadvantages of several existing container positioning methods, and proposed a container corner holes location detection method based on lightweight convolutional neural network and adaptive morphological image processing algorithm. This method locates the container through a visual sensor that shoots from top to bottom. In order to improve the positioning accuracy and calculation speed while maintaining a high recognition rate, this method first uses a lightweight SSD detector to quickly detect the rough coordinates of the container corner hole in the image. On this basis, the smallest rectangle detection method based on adaptive HSV filtering is used to detect the precise coordinates of the corner hole in the image. Experiments show that the recognition rate of this algorithm in the initial positioning process reaches 94.2%, the recognition rate of secondary positioning reaches 87.4%, and the final positioning error is lower than 3.765 pixels and 2.81 pixels in the heading and lateral directions, respectively. The overall calculation time of the algorithm can realize the positioning calculation of 30 frames per second, which shows that this method takes into account the characteristics of high measurement accuracy and high calculation speed on the basis of high recognition rate. [ABSTRACT FROM AUTHOR]

The water medium explosion container is an experimental device that simulates explosion in different water depth environments by loading different hydrostatic pressures and different doses of explosive. To ensure its safety during service, it is necessary to study the dynamic response of water medium explosion container. Because the dynamic response is complicated and the correlation between the response and the load of the container is nonlinear, it is difficult to calculate the dynamic response by analytical and numerical methods. In this paper, a model is built based on convolutional neural network (CNN) to predict the dynamic response of water medium explosion container. The accuracy and usability of the CNN prediction model are verified by comparison with the prediction results of the BP neural network model. The results show that CNN can be effectively used to predict the strain response of the dynamic response of water medium explosion container. and this method will play an important role in the later study of the overall feature analysis of the dynamic response of the water medium explosion vessel. [ABSTRACT FROM AUTHOR]