Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++

Cultivar discrimination of litchi fruit images using deep learning

A lightweight dead fish detection method based on deformable convolution and YOLOV4

The ocean is an important ecosystem, and aquatic animals play an important role in the biological world, especially in aquaculture. How to accurately and intelligently recognise and detect aquatic animals is one of the urgent problems in the field of underwater biological detection. The wide applications of artificial intelligence (AI), especially deep learning (DL), provide new opportunities and challenges for the efficient and intelligent exploration of aquatic animals. DL has been widely used in the visual recognition and detection of terrestrial animals, but it is in the early stages of use for aquatic animals due to the complexity of underwater environment and the difficulty of data acquisition. Here, this article reviews the current application status of DL for aquatic animals, potential challenges and future directions. The key advances of DL algorithms applied to the visual recognition and detection of aquatic animals are generalised, including datasets, algorithms and performance. The applications of DL are summarised in aquatic animals, including image detection, video detection, species classification, biomass estimation, behaviour analysis and food safety. Furthermore, the challenges are summed up and classified in the object recognition and detection domain for aquatic animals. Finally, further research direction is discussed and the conclusions are drawn. The key advances of DL in the recognition and detection of aquatic animals will help to further excavate and extend the application of DL in the field of marine biological exploration. 

Deep learning for visual recognition and detection of aquatic animals: A review

Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review

Fish behavior has attracted increasing attention in global aquaculture because it provides important information about productivity and fish quality. The use of images to detect fish behavior has shown potential in aquaculture behavioral studies by providing higher spatial resolution, efficiency, and accuracy than conventional approaches such as manual measurement. In addition, it allows for more quantitative data analysis than do other methods. To date, conventional image processing approaches to monitor fish behavior have been based primarily on appearance, morphology, and color information. This approach is complex and/or time-consuming and limits the practicality of such methods in aquaculture. To address these problems, we present herein a noninvasive, rapid, low-cost procedure based on an underwater imaging system and a deep learning framework to detect fish behavior with high accuracy in a mixed polyculture system. The specific objectives of this study are (1) to design a low-cost underwater imaging system that can describe and quantify fish behavior via visual images, and (2) to develop a lightweight deep learning structure to rapidly and accurately detect fish behavior under various conditions. Toward this end, images of fish are first captured via a low-cost imaging system, following which they are preprocessed to reduce noise and enhance data information. Finally, an improved You Only Look Once version 3 Lite (YOLOv3-Lite) network with a novel backbone structure is used to improve the pooling block and loss function and thereby better recognize fish behavior. The proposed method was tested on a real dataset and produced a Precision of 0.897, a Recall of 0.884, an intersection over union of 0.892, and 240 frames per second. Furthermore, when compared with faster region-convolutional neural network, YOLO, YOLOv2, YOLOv3, and single shot multi-Box detector, the performance of each evaluation metric of the proposed method was improved by 10%–20%. This comprehensive analysis indicates that the proposed method provides state-of-the-art performance and may be used in fish farms.

Real-time nondestructive fish behavior detecting in mixed polyculture system using deep-learning and low-cost devices

The invention discloses an automatic squid classification method based on a color image and a convolutional neural network technology, which comprises the following steps: unfreezing squids, cleaningthe unfrozen squids, removing pollutants on the surfaces of the squids, and removing damaged squids to prepare squid samples for automatic classification of the squids; flatly unfolding the squid sample on work, placing the squid sample in an auxiliary light source irradiation area, and performing image acquisition on the squid sample at different angles by using shooting equipment to obtain an original squid image; carrying out image preprocessing on the original squid image to obtain a test image; inputting the test image into an improved convolutional neural network for training to obtain image features of different types of squids so as to realize squid classification and recognition. According to the method, the machine vision technology and the deep learning framework are combined, the features of the squids in the image are automatically extracted through the improved convolutional neural network, correct classification of different varieties is achieved, and therefore the requirement for fine processing in industrialized production is met.

Automatic squid classification method based on color image and convolutional neural network technology

The invention discloses a squid freshness identification method based on color space transformation and pixel clustering, and the method comprises the steps: unfreezing squids, cleaning the unfrozen squids, and preparing squid samples; unfolding the squid sample on a workbench, placing the squid sample in an auxiliary light source irradiation area, and performing image acquisition on the squid sample at different angles by using shooting equipment to obtain an original squid image; carrying out image preprocessing to obtain a test image; carrying out color space transformation and pixel clustering on the test image, extracting a red decay area of the test image, carrying out ratio calculation on the red decay area and the total surface area of the squid, dynamically analyzing the meat quality change condition of the squid, and monitoring the decay rate of the squid. According to the method, the image processing technology is utilized, color space transformation and pixel clustering analysis are carried out on the shot squid images to obtain the metamorphic region area of the squid images, ratio calculation is carried out on the metamorphic region area and the total surface area, and therefore non-contact and lossless squid freshness dynamic monitoring and identification under different storage durations and temperatures are achieved.

Squid freshness identification method based on color space transformation and pixel clustering

A rapid, low-cost deep learning system to classify squid species and evaluate freshness based on digital images

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chengquan Zhou

Author Tools

Chat about Author

Papers

Real-time nondestructive fish behavior detecting in mixed polyculture system using deep-learning and low-cost devices

Automatic squid classification method based on color image and convolutional neural network technology

Squid freshness identification method based on color space transformation and pixel clustering

A rapid, low-cost deep learning system to classify squid species and evaluate freshness based on digital images