Research on Marine Life Detection Algorithm Based on Deep Learning

Target detection and segmentation algorithms have long been one of the main research directions in the field of computer vision, especially in the study of sea surface image understanding, these two tasks often need to consider the collaborative work at the same time, which is very high for the computing processor performance requirements. This article aims to study the deep learning sea target detection and segmentation algorithm. This paper uses wavelet transform-based filtering method for speckle noise suppression, deep learning-based method for land masking, and the target detection part uses an improved CFAR cascade algorithm. Finally, the best separable features are selected to eliminate false alarms. In order to further illustrate the feasibility of the scheme, this paper uses measured data and simulation data to verify the scheme and discusses the effect of different signal-to-noise ratio, sea target type, and attitude on the algorithm performance. The research data show that the deep learning sea target detection and segmentation algorithm has good detection performance and is generally applicable to ship target detection of different types and different attitudes. The results show that the deep learning sea target detection and segmentation algorithm fully takes into account the irregular shape and texture of the interfering target detected in the optical remote sensing image so that the accuracy rate is 32.7% higher and the efficiency is increased by about 1.3 times. The deep learning sea target detection is compared with segmentation algorithm, and it has strong target characterization ability and can be applied to ship targets of different scales.

For video surveillance analysis, target detection algorithms are particularly important. Aiming at the problem of target detection in road traffic scenes, this paper proposes a non-motor vehicle target detection method that uses EdgeBoxes algorithm and deep learning fusion. It is inspired by deep learning target classification algorithm Fast R-CNN, and combines non-motor vehicle data samples in VOC format to integrate road traffic scenes. We use the EdgeBoxes algorithm to extract the target of the sample. It is recommended to construct an appropriate amount of region of interest, and inputs the network for iterative training with the sample. In the target detection of road traffic scenes, compared with the traditional method based on the fusion of the basic EdgeBoxes algorithm and the optimized Fast R-CNN method, the detection accuracy is slightly improved, the calculation is significantly reduced, and the algorithm has advantages in time complexity.

In order to test the deep-learning YOLOv4 target detection algorithm's effect on excavator target detection in aerial photography environment, a fast target detection method for excavators in aerial photography environment was simulated. Based on the Pytorch deep learning framework and the deep learning YOLOv4 (You Only Look Once version 4) target detection algorithm, through the production of excavator aerial photography data sets, the simulation realized the rapid target detection of excavators in the aerial photography environment, and its target recognition AP value reached 0.72. Based on the same data set, its detection accuracy and speed exceeded Traditional algorithms such as Faster RCNN are used. The simulation results can provide a certain reference for the research of aerial inspection and maintenance of underground pipeline facilities.

As an important part of modern marine monitoring systems, ship target identification has important significance in maintaining marine rights and monitoring maritime traffic. With the development of artificial intelligence technology, image detection and recognition based on deep learning methods have become the most popular and practical method. In this paper, two deep learning algorithms, the Mask R-CNN algorithm and the Faster R-CNN algorithm, are used to build ship target feature extraction and recognition models based on deep convolutional neural networks. The established models were compared and analyzed to verify the feasibility of target detection algorithms. In this study, 5748 remote sensing maps were selected as the dataset for experiments, and two algorithms were used to classify and extract warships and civilian ships. Experiments showed that for the accuracy of ship identification, Mask R-CNN and Faster R-CNN reached 95.21% and 92.76%, respectively. These results demonstrated that the Mask R-CNN algorithm achieves pixel-level segmentation. Compared with the Faster R-CNN algorithm, the obtained target detection effect is more accurate, and the performance in target detection and classification is better, which reflects the great advantage of pixel-level recognition.

Target detection is a research hotspot in the field of computer vision. The target detection algorithm based on candidate regions has high accuracy, but the structure is complex, and the detection speed is slow. The regression-based target detection algorithm has simple structure and fast detection speed, and it is in real-time target detection The field has high application value, but the detection accuracy is relatively low. Based on this research background, this paper first analyses the mechanism of shadow generation, understands the characteristics of shadows and human visual characteristics, and summarizes and classifies the current existing features. Based on the similar shadow detection algorithm, a shadow detection algorithm based on the RGB colour model is proposed. The algorithm in this paper is verified through experiments. The article proves that the algorithm can detect and remove the shadows of moving targets well.

Purpose In order to resolve the situation of high missed diagnosis rate and high misdiagnosis rate of the pathological analysis of the gastrointestinal endoscopic images by experts, we propose an automatic polyp detection algorithm based on Single Shot Multibox Detector (SSD). Method In the paper, SSD is based on VGG-16, the fully connected layer is changed to a convolutional layer, and four convolutional layers with successively decreasing scales are added as a new network structure. In order to verify the practicability, it is not only compared with manual polyp detection but also with Mask R-CNN. Results Multiple experimental results show that the mean Average Precision (mAP) of the SSD network is 95.74%, which is 12.4% higher than the manual detection and 5.7% higher than the Mask R-CNN. When detecting a single frame of image, the detection speed of SSD is 8.41 times that of manual detection. Conclusion Based on the traditional pattern recognition algorithm and the target detection algorithm using deep learning, we select a variety of algorithms to identify and classify polyps to achieve efficient detection results. Our research demonstrates that deep learning has a lot of room for development in the field of gastrointestinal image recognition.

Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems.

Deep learning two-stage target detection algorithms have been widely used in the field of target detection relying on the advantages of high accuracy. This paper introduces four common two-stage target detection algorithms in detail: R-CNN, Fast R-CNN, Faster R-CNN and Mask R-CNN, and expounds their respective algorithm steps, advantages and disadvantages. At the same time, the feature extraction network that is often used in the current two-stage target detection algorithm is analyzed and summarized from two directions. One direction is to pursue image classification networks with deeper and more complex network structures and higher detection accuracy for feature extraction, such as AlexNet, VGGNet, GoogleNet, ResNet and DenseNet. Another direction is to solve the problem that the network parameters are too large and the detection speed is too slow, a lightweight neural network is proposed, hoping to reduce the parameter calculation amount of the neural network and speed up the detection speed of the network, such as SqueezeNet, MobileNet, ShuffleNet and GhostNet. This paper introduces the network structure of the above nine networks and their respective advantages and disadvantages from the above two directions, and provides a theoretical and method reference for the selection of the feature extraction network of the two-stage target detection algorithm.

Objective The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

There are two main approaches to hyperspectral target detection: anomaly detection techniques, which detect outliers substantially different from the background, and spectral signature techniques, which require as an input a user-defined target signature. Oftentimes, however, the target signature may not be known, or there may be unexpected targets in the image, which are unknown but still of interest. As a result, algorithms that can automatically extract potential target signatures without any a priori knowledge are of great interest. In this work, a fusion-based algorithm is developed that takes advantage of both spatial and spectral information to automatically extract the spectral signatures of potential targets of interest. The performance of several target detection algorithms is compared for both the proposed spatially-spectrally estimated (SSE) target signature and the initial target signature used by the Automatic Target Detection and Classification Algorithm (ATDCA). It is shown that the SSE signature leads to improved automatic spectral target recognition (ATSR) performance than the ATDCA algorithm for the test conducted on the AVIRIS Indian Pines dataset.

Agriculture currently serves as a crucial food source for the global population. However, coconut farming, in particular, demands extensive care and maintenance. This research aims to classify coconut trees across various plantation areas utilizing deep learning techniques, specifically through Faster R-CNN and Mask R-CNN models, based on unmanned aerial vehicle (UAV) imagery. The data collected by both types of RGB UAVs was used for the classification of coconut trees in experimental plots. For the analysis process, aerial photographs obtained from unmanned aerial vehicles, merged with the principles of aerial photography measurement, were analyzed. The research findings revealed that both Faster R-CNN and Mask R-CNN were capable of effectively classifying image data. Nevertheless, to achieve higher accuracy in results, it is essential that the characteristics of the test plots closely align with each other. This study points towards the adoption of a high-resolution tool, ensuring clearer images that facilitate more accurate classification of coconut trees across extensive areas. Consequently, this could lead to more efficient management and maintenance of coconut plantations. Thus, this approach can substantially enhance the efficiency of managing coconut plantations. Doi: 10.28991/HEF-2024-05-04-02 Full Text: PDF

This paper proposes an intelligent digital modeling system based on computer vision technology, aiming to realize real-time analysis and modeling of sports games. This study uses deep learning technology, combined with motion target detection and action recognition algorithms, to build an automated and intelligent sports game modeling system. The system captures game data in real time through video surveillance equipment, and uses target detection algorithms such as YOLO and SSD to identify athletes, balls and other game elements. At the same time, it realizes the accurate capture of athlete movements and tactical intentions based on the algorithm of posture estimation and action recognition. In terms of experimental verification, this paper takes football games as an example. The accuracy of the system in motion target detection is 92.5%, and the accuracy in athlete action recognition is 87.3<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>• In addition, the real-time modeling accuracy of the system is controlled within 5%, and the processing delay is less than 200 milliseconds, which meets the requirements of real-time analysis. Through the analysis of experimental results, this study proves the efficiency and accuracy of the system in complex sports game environments.

Target detection is now a popular topic in computer vision. With the development and iteration of technology, deep learning is constantly emerging. The integration of deep learning in the target detection task has led to rapid improvement in accuracy and speed, among which the You Only Look Once (YOLO) series of methods have the most rapid and varied improvements and upgrades, which have been widely used in the fields of navigation, video surveillance, face detection, text detection, and aerospace, etc. This paper initially provides an overview of the research context, importance, and challenges associated with this domain, compares and analyzes the network structure and implementation of the single-phase target detection method represented by the YOLO series with the two-phase and other improved algorithms, and then introduces the research progress of the target detection algorithms of deep learning, the characteristics of the commonly used datasets, and the key parameters of the evaluation of performance indicators, and then presents a compilation of experimental outcomes associated with several widely recognized algorithms applied to prominent datasets. Subsequently, it enumerates the experimental findings of diverse algorithms on these established datasets. Ultimately, this paper anticipates future research trajectories and developmental trends pertaining to target detection algorithms.

Deep learning target detection based on faster regions with convolutional neural network (Faster R-CNN) features has been applied in image processing successfully, however, it is rarely introduced to the field of hyperspectral image (HSI) target detection due to the tensor characteristics and the lack of training samples of HSI data. In this paper, the target detection based on Faster R-CNN is proposed to HSI with data set adjustment and parameter turning. As a typical tensor data, HSIs contain two-dimensional (2-D) spatial information and one dimensional (1-D) spectral information. It contains more information than ordinary images, and has unique advantages in the field of ground object and sea target detection. Therefore, the original HSI is firstly adjusted to the data set format required by the model, and the final Faster R-CNN sample data set can be achieved by combining the data set of Google Earth images. Next, a Faster R-CNN network suitable for HSI data could be built. Finally, to improve the accuracy of target detection, some parameters of Faster R-CNN would be tuned. The numerical results show that the method has the potential advantages of high precision and high speed in HSI target detection, and will have broad application prospects.

Due to the nonlinearity of the imaging of sonar equipment and the complexity of the underwater sound field environment, the gray level of the target area of the acquired underwater sonar image is relatively small. These characteristics are the target of the subsequent sonar image. Work such as detection and location tracking has brought great challenges. It has brought great challenges to solving the work of positioning and tracking, which makes the research of sonar image target detection based on deep learning very important. This article aims at studying the use of sonar to detect image targets based on deep learning technology. This article proposes a variety of sound image denoising methods based on multiresolution tools. The purpose of this article is to divide the natural image into blocks at an appropriate rate according to the change of the sampling matrix and measure the underwater natural image. The sound image defines an information model. These methods have greatly changed the image and period of using remote and temporary information. The translation results of these methods are all valid. The sharpening separation method based on filtered image and bidirectional detection should be published through a solution algorithm and different frames, and the expected algorithm can be reused and extracted as an action to improve the similarity of the image and should be saved and separated in detail. The result is correct. This article studies the application of deep learning methods in sonar image target detection and designs corresponding algorithms for improvement and functional realization in view of the current deficiencies and needs in this field. The experimental results show that the improved scheme and applied algorithm proposed in this article can achieve good results, the verification sample set includes 184 remote-sensing aircraft targets, and the resolution of remote-sensing images is unified to 1644 × 971 size. The accuracy of the target detection algorithm has been significantly improved, reaching 74.6%, and the detection speed has also been greatly improved. Compared with the RNN algorithm, the speed has been increased by 7 times. The detection results confirmed that the improved algorithm has higher positioning accuracy and faster detection speed.