Sep 14, 2019 · This paper presents a novel methodology for detecting the intrusion of wild animals using deep neural networks with multishift spatio-temporal features from surveillance …

6 days ago · Animal Detection in Man-made Environments using Deep Learning. A U-Net-based deep learning ensemble model for wildebeest-sized animal detection from satellite imagery.

Apr 1, 2024 · After summarizing the deep learning methods, we identified the primary challenges in animal detection: data imbalance, small samples, small objects, image annotation methods, …

Wildlife animal detection is crucial for ecological monitoring, conservation, and minimizing human-wildlife conflict. This study explores the application of deep learning algorithms for accurate …

The collision of vehicles with animals is an emerging threat to humans and wildlife. Efficient observation of wild animals is crucial. Cost-effective techniques.

Furthermore, this comprehensive review has demonstrated that a more sustainable and balanced relationship between humans and animals can be achieved by harnessing the power of deep …

we're introducing an automatic relating and counting the animals by using deep learning methods. This process gives an accurate result to detects and keep proper count of animals.

Mar 28, 2020 · By using Convolutional Neural Network (CNN) architectures, we can train a system capable of filtering images from these datasets and identifying its objects …

Jan 1, 2023 · In this study, we propose the utilization of the YOLOv8 architecture to detect four distinct categories: Lions, Tigers, Leopards, and Bears. The dataset is constructed from …

Dec 1, 2021 · deep learning ideas relevant to the detection and recognition of wildlife animals, as well as the contributions to the field, the majority of which have been published recently.