A video-based method to quantify animal posture movement is a powerful way to analyze animal behavior. Both humans and fish can judge the physiological state through the skeleton framework. However, it is challenging for farmers to judge the breeding state in the complex underwater environment. Therefore, images can be transmitted by the underwater camera and monitored by a computer vision model. However, it lacks datasets in artificial intelligence and is unable to train deep neural networks. The main contributions of this paper include: (1) the world’s first fish posture database is established. 10 key points of each fish are manually marked. The fish flock images were taken in the experimental tank and 1000 single fish images were separated from the fish flock. (2) A two-stage attitude estimation model is used to detect fish key points. The evaluation of the algorithm performance indicates the precision of detection reaches 90.61%, F1-score reaches 90%, and Fps also reaches 23.26. We made a preliminary exploration on the pose estimation of fish and provided a feasible idea for fish pose estimation.
Due to memory and computing resources limitations, deploying convolutional neural networks on embedded and mobile devices is challenging. However, the redundant use of the 1 × 1 convolution in traditional light-weight networks, such as MobileNetV1, has increased the computing time. By utilizing the 1 × 1 convolution that plays a vital role in extracting local features more effectively, a new lightweight network, named PlaneNet, is introduced. PlaneNet can improve the accuracy and reduce the numbers of parameters and multiply-accumulate operations (Madds). Our model is evaluated on classification and semantic segmentation tasks. In the classification tasks, the CIFAR-10, Caltech-101, and ImageNet2012 datasets are used. In the semantic segmentation task, PlaneNet is tested on the VOC2012 datasets. The experimental results demonstrate that PlaneNet (74.48%) can obtain higher accuracy than MobileNetV3-Large (73.99%) and GhostNet (72.87%) and achieves state-of-the-art performance with fewer network parameters in both tasks. In addition, compared with the existing models, it has reached the practical application level on mobile devices. The code of PlaneNet on GitHub: https://github.com/LinB203/planenet.
The sex ratio is an important factor affecting the economic benefits of duck groups in the process of hemp duck breeding. However, the current manual counting method is inefficient, and the results are not always accurate. On the one hand, ducks are in constant motion, and on the other hand, the manual counting method relies on manpower; thus, it is difficult to avoid repeated and missed counts. In response to these problems, there is an urgent need for an efficient and accurate way of calculating the sex ratio of ducks to promote the farming industry. Detecting the sex ratio of ducks requires accurate counting of male ducks and female ducks. We established the world’s first manually marked sex classification dataset for hemp ducks, including 1663 images of duck groups; 17,090 images of whole, individual duck bodies; and 15,797 images of individual duck heads, which were manually captured and had sex information markers. Additionally, we used multiple deep neural network models for the target detection and sex classification of ducks. The average accuracy reached 98.68%, and with the combination of Yolov5 and VovNet_27slim, we achieved 99.29% accuracy, 98.60% F1 score, and 269.68 fps. The evaluation of the algorithm's performance indicates that the automation method proposed in this paper is feasible for the sex classification of ducks in the farm environment, and is thus a feasible tool for sex ratio estimation.
The accuracy of fish farming and real-time monitoring are essential to the development of “intelligent” fish farming. Although the existing instance segmentation networks (such as Maskrcnn) can detect and segment the fish, most of them are not effective in real-time monitoring. In order to improve the accuracy of fish image segmentation and promote the accurate and intelligent development of fish farming industry, this article uses YOLOv5 as the backbone network and object detection branch, combined with semantic segmentation head for real-time fish detection and segmentation. The experiments show that the object detection precision can reach 95.4% and the semantic segmentation accuracy can reach 98.5% with the algorithm structure proposed in this article, based on the golden crucian carp dataset, and 116.6 FPS can be achieved on RTX3060. On the publicly available dataset PASCAL VOC 2007, the object detection precision is 73.8%, the semantic segmentation accuracy is 84.3%, and the speed is up to 120 FPS on RTX3060.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.