Sign Language Translation and Voice Impairment Support System using Deep Learning
Shajeena J, Shiny R M, Mary Vespa M, K. R
2024
User Interface based Text-To-Speech Synthesizer
Yatesh Khandelwal, Reema Goyal, Poonam Negi
2024
Efficient Real-time Book Cover Text Extraction with YOLOv8 and SpaCy
Nawal Alghemei, Hasan Alkhadafe, Ibrahim Nasir
2024
Retail Enhancement Using Computer Vision Models
Ghassan Zgorni, Adham Qussay, Salma Elmasry, Renada Ayman, Sama Amr Habib, Magi Hossam
2024
Performance Evaluation of YOLOv8 and YOLOv9 for Object Detection in Remote Sensing Images
Mahinar M. Reda, Dina M. El Sayad, Noureldin Laban, Mohamed F. Tolba
2024
YOLOv5-KCB: A New Method for Individual Pig Detection Using Optimized K-Means, CA Attention Mechanism and a Bi-Directional Feature Pyramid Network
Guangbo Li, Guolong Shi, Jun Jiao
2023
Automated Individual Pig Localisation, Tracking and Behaviour Metric Extraction Using Deep Learning
Jake Cowton, I. Kyriazakis, J. Bacardit
2019
EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations
Jihyun Seo, Hanse Ahn, Daewon Kim, Sungju Lee, Yongwha Chung, Daihee Park
2020
Yolov5-based defect detection for wafer surface micropipe
Ning Zhou, Zhengxin Liu, Jianxin Zhou
2022
RPS-YOLO: A Recursive Pyramid Structure-Based YOLO Network for Small Object Detection in Unmanned Aerial Vehicle Scenarios
Penghui Lei, Chenkang Wang, Peigang Liu
2025
Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras
Aniket Shirke, Aziz Saifuddin, Achleshwar Luthra, Jiangong Li, Tawni N Williams, Xiaodan Hu, Aneesh Kotnana, Okan Kocabalkanli, N. Ahuja, Angela Green-Miller, Isabella C. F. S. Condotta, R. Dilger, M. Caesar
2021
Research on improved YOLOv8 remote sensing target detection algorithm based on multi-receptive field feature enhancement
Pig detection is vital to pig farms since it is the basis for counting, weight estimation, and behavior recognition functions. Existing methods focused on white pig detection instead of black pigs because the contrast between white pigs and the background is more evident than black pigs, making black pig detection more challenging. Furthermore, pig farms often suffer from insufficient light, and pigs tend to be stacked together, which makes it hard to detect pigs accurately. To this end, we propose a black pig detection method, based on YOLOV4, robust to both crowd scenes and low-illumination conditions. The method consists of the following parts: 1) generate domain adaptation dataset (DAD) based on style-transfer to optimize the original data distribution, therefore improving the performance of the method for low-illuminated conditions; 2) propose a crowd-aware module (CAM), adapted to YOLOV4 backbone architecture, to generate crowd density maps; 3) develop an adaptive attention module (AAM) to fuse YOLOV4 backbone features with corresponding crowd density map allowing the method robust to pigs in the crowd. The experimental results confirm the feasibility of this method. The mAP value in fully-lighting and poorly-lighting increased to 88.95% and 84.74%, respectively.