A multitask model for realtime fish detection and segmentation based on YOLOv5.

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.

A Two-Stage Method to Detect the Sex Ratio of Hemp Ducks Based on Object Detection and Classification Networks.

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.

Research on remote sensing image extraction based on deep learning.

Remote sensing technology has the advantages of fast information acquisition, short cycle, and a wide detection range. It is frequently used in surface resource monitoring tasks. However, traditional remote sensing image segmentation technology cannot make full use of the rich spatial information of the image, the workload is too large, and the accuracy is not high enough. To address these problems, this study carried out atmospheric calibration, band combination, image fusion, and other data enhancement methods for Landsat 8 satellite remote sensing data to improve the data quality. In addition, deep learning is applied to remote-sensing image block segmentation. An asymmetric convolution-CBAM (AC-CBAM) module based on the convolutional block attention module is proposed. This optimization module of the integrated attention and sliding window prediction method is adopted to effectively improve the segmentation accuracy. In the experiment of test data, the mIoU, mAcc, and aAcc in this study reached 97.34%, 98.66%, and 98.67%, respectively, which is 1.44% higher than that of DNLNet (95.9%). The AC-CBAM module of this research provides a reference for deep learning to realize the automation of remote sensing land information extraction. The experimental code of our AC-CBAM module can be found at https://github.com/LinB203/remotesense.

PlaneNet: an efficient local feature extraction network.

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.