To improve the navigation safety of inland river ships and enrich the methods of environmental perception, this paper studies the recognition and depth estimation of inland river ships based on binocular stereo vision (BSV). In the stage of ship recognition, considering the computational pressure brought by the huge network parameters of the classic YOLOv4 model, the MobileNetV1 network was proposed as the feature extraction module of the YOLOv4 model. The results indicate that the mAP value of the MobileNetV1-YOLOv4 model reaches 89.25%, the weight size of the backbone network was only 47.6 M, which greatly reduced the amount of computation while ensuring the recognition accuracy. In the stage of depth estimation, this paper proposes a feature point detection and matching algorithm based on the ORB algorithm at sub-pixel level, that is, firstly, the FSRCNN algorithm was used to perform super-resolution reconstruction of the original image, to further increase the density of image feature points and detection accuracy, which was more conducive to the calculation of the image parallax value. The ships’ depth estimation results indicate that when the distance to the target is about 300 m, the depth estimation error is less than 3%, which meets the depth estimation needs of inland ships. The ship target recognition and depth estimation technology based on BSV proposed in this paper makes up for the shortcomings of the existing environmental perception methods, improves the navigation safety of ships to a certain extent, and greatly promotes the development of intelligent ships in the future.

/pdf/recognition-and-depth-estimation-of-ships-based-on-binocular-2ca9952y.pdf

Recognition and Depth Estimation of Ships Based on Binocular Stereo Vision

Digital media art is a new type of art with rich sensory experience. Nowadays, digitization is flooding many areas of life. Under the sprint of a large amount of information, human beings have once again entered the digital age. This article is aimed at studying the fusion of oil painting art and digital media based on visual sensors, analyzing the application of digital imaging art using sensor technology in various fields, especially in the field of oil painting art, and analyzing the effects of digital media with the support of sensor technology. The artistic characteristics and rich forms of presentation are presented, and then, the key points of digital media design are summarized. This paper proposes to bring advanced sensor technology into the field of art for research, turning boring and difficult technical data into an interesting art form, making human-computer interaction more compact and humane, and creating better works of art, so that more and more people and different fields can enjoy the scientific and technological achievements. The sensor is a kind of bionics in modern science, which enables machinery to perceive the human environment like human or animal sensory organs, through the perception and detection of this environmental change, and writing a certain program, the signal data is converted into electricity or signal, and at the same time transmit these signals to receiving organs or devices, such as device sensors that simulate organs. The art of digital photography requires the intervention of sensor technology to make interaction and virtue more complete. The sensor technology also requires the art of digital photography to provide an external display window, which can better serve mankind and create greater social value. The combination of technology and art makes the presentation of art more distinctive. The experimental results of this paper show that the integration of oil painting art and digital media based on visual sensors has made digital media have an impact on more than 58% of oil painting art works and made many oil painting art works show an unprecedented sense of science and technology, which is important for future oil painting art. The development of the company has positive significance.

/pdf/study-on-the-fusion-of-oil-painting-art-and-digital-media-s6sla9mh.pdf

Study on the Fusion of Oil Painting Art and Digital Media Based on a Visual Sensor

For automated grinding, such as robotic grinding, high stability of the manufacturing system is required during the machining process. Little attention has been paid to the effect of the uniformity of the matching margin on the machining stability as a result of visual matching. In fact, it has an important relationship with maintaining the stability of robotic grinding and keeping the uniformity of grinding depth. In this paper, important process features of the blade part are extracted to improve the matching efficiency with a point cloud, while the normal direction of the contour after finishing grinding is used as the normal direction for registration. The objective function of minimization of the allowance variance is constructed to improve the uniformity of the grinding depth. The overall uniformity of the calculation results is also considered, and the effect of no tangential distance tampering is achieved by constraining the shape centers of the two point cloud models. The method in this paper is compared with the classical matching method to verify the feasibility of the margin variance minimization algorithm based on the decentered point cloud edges in terms of algorithm speed, convergence, accuracy, spatial complexity, and the uniformity of the obtained results. 

A uniform allowance matching method for point cloud based on the edge extraction under de-shaping center

The spatial location of fuze detonation point is crucial for evaluating the working condition and improving the performance of fuze. Considering the observation safety, the non-contact long-distance accurate measurement technology is essential. In this paper, we propose a method that takes the sensor data of optics, spatial attitude and GPS as input and outputs the spatial position of the fuze detonation point. The proposed method consists of two steps. First, an object detection algorithm with post-processing algorithm is proposed to obtain rich information of the target. The algorithm achieves high-accuracy detection by introducing powerful backbone, attention mechanism, group convolution, and improved multi-scale feature fusion. Second, a Variational Auto-Encoder (VAE) algorithm model improved by dense connection structure and multiple source heterogeneous sensor information fusion structure is proposed as the position regression algorithm. It receives the status information of the observer camera and the output of the object detection algorithm, and then outputs the three-dimensional coordinates of the explosion point. Finally, method validation and performance analysis are realized through virtual scene simulation. Experiment results show the superiority of the proposed object detection algorithm over other typical algorithms on explosion flare detection, with its Average Precision (AP) of 0.889. The positioning error of the spatial location method is 0.896 m, while that of the binocular stereo vision method is 2.863 m. Therefore, the proposed target spatial location method is proved to be effective and accurate. 

A target spatial location method for fuze detonation point based on deep learning and sensor fusion

During the overhead crane, due to the complex background and large image size, the direct processing algorithm usually leads to time-consuming operation and difficult to achieve accurate personnel positioning. To address these issues, we propose the feature points recognition and matching algorithm incorporating object edge detection and scale-invariant feature transform (SIFT), which is applied to the designed object detection and positioning algorithm for binocular stereo vision. The proposed method applies the Canny operator to extract contours from the locally cropped regions obtained using YOLOv5. The contour points are used as the key points of SIFT, and corresponding feature points are created descriptors, and feature points matching are performed. Experimental results indicate that the proposed method can filter feature points successfully. Moreover, the binocular stereo vision system with the binocular positioning algorithm designed has good performance, with the overall depth direction positioning error within 5% in a range of 12 m. 

A system and method for person identification and positioning incorporating object edge detection and scale-invariant feature transformation

Based on binocular stereo vision measurement and wireless sensing theory, this paper studies the measurement and analysis methods of athletes' movement displacement parameters The main purpose is to explore the relationship between sports biomechanical parameters The analysis reveals the laws of athletes 'movements, and provides theoretical guidance on athletes' attitude recognition and physical training Based on the principle of binocular stereo vision, a non-contact displacement measurement system was developed The displacement measurement system uses template matching to extract the image coordinates of the measurement points and restore the spatial coordinates of the measurement points through European 3D reconstruction, which improves the practicality of the measurement system In the cantilever beam vibration test, the traditional displacement sensor and binocular image displacement measurement system are used to simultaneously measure the cantilever beam displacement The experimental results show that the integrated displacement measurement system in this paper has higher accuracy and better stability

Application of integrated binocular stereo vision measurement and wireless sensor system in athlete displacement test

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Application of integrated binocular stereo vision measurement and wireless sensor system in athlete displacement test