Related papers: Cattle Detection Occlusion Problem

Severe weather events can cause large financial losses to farmers. Detailed information on the location and severity of damage will assist farmers, insurance companies, and disaster response agencies in making wise post-damage decisions.…

This article compares the performance of six prominent object detection algorithms, YOLOv11, RetinaNet, Fast R-CNN, YOLOv8, RT-DETR, and DETR, on the NEU-DET surface defect detection dataset, comprising images representing various metal…

This paper proposes and evaluates, for the first time, a top-down (dorsal view), depth-only deep learning system for accurately identifying individual cattle and provides associated code, datasets, and training weights for immediate…

The safety of wind turbines is a prerequisite for the stable operation of offshore wind farms. However, bird damage poses a direct threat to the safe operation of wind turbines and wind turbine blades. In addition, millions of birds are…

Animal welfare has become a critical issue in contemporary society, emphasizing our ethical responsibilities toward animals, particularly within livestock farming. The advent of Artificial Intelligence (AI) technologies, specifically…

Traditional animal identification methods such as ear-tagging, ear notching, and branding have been effective but pose risks to the animal and have scalability issues. Electrical methods offer better tracking and monitoring but require…

Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical, and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on…

Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain. The advanced technologies of machine learning and computer vision have been…

In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude. In each spectrum, a…

Predominant methods for image-based drone detection frequently rely on employing generic object detection algorithms like YOLOv5. While proficient in identifying drones against homogeneous backgrounds, these algorithms often struggle in…

In this work we consider the task of detecting sheep onboard an unmanned aerial vehicle (UAV) flying at an altitude of 80 m. At this height, the sheep are relatively small, only about 15 pixels across. Although deep learning strategies have…

Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access…

Cattle farming is one of the important and profitable agricultural industries. Employing intelligent automated precision livestock farming systems that can count animals, track the animals and their poses will raise productivity and…

Cattle activity is an essential index for monitoring health and welfare of the ruminants. Thus, changes in the livestock behavior are a critical indicator for early detection and prevention of several diseases. Rumination behavior is a…

This study explores a comprehensive approach to obstacle detection using advanced YOLO models, specifically YOLOv8, YOLOv7, YOLOv6, and YOLOv5. Leveraging deep learning techniques, the research focuses on the performance comparison of these…

You Only Look Once (YOLO) is a single-stage object detection model popular for real-time object detection, accuracy, and speed. This paper investigates the YOLOv5 model to identify cattle in the yards. The current solution to cattle…

This study addresses the demand for real-time detection of tomatoes and tomato flowers by agricultural robots deployed on edge devices in greenhouse environments. Under practical imaging conditions, object detection systems often face…

In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks (CNN). This problem presents additional challenges as compared to car (or any object) detection from ground images because…

Anthropogenic activities pose threats to wildlife and marine fauna, prompting the need for efficient animal counting methods. This research study utilizes deep learning techniques to automate counting tasks. Inspired by previous studies on…

This study compares the performance of state-of-the-art neural networks including variants of the YOLOv11 and RT-DETR models for detecting marsh deer in UAV imagery, in scenarios where specimens occupy a very small portion of the image and…