Results 61 to 70 of about 1,105,657 (336)

Moving in the Dark: Enlightening the Spatial Population Ecology of European Cave Salamanders

Population Ecology, EarlyView.
We assessed individual interactions, movement ecology and activity patterns of a subterranean population of Speleomantes strinatii, applying spatial capture–recapture modeling to a photographic dataset of 104 individuals. ABSTRACT Space use and movement are fundamental aspects of organisms' ecology, mirroring individual fitness, behavior, and life ...
Giacomo Rosa, Andrea Costa, Sebastiano Salvidio   +2 more
wiley   +1 more source

Enhanced Lightweight Object Detection Model in Complex Scenes: An Improved YOLOv8n Approach

Information
Object detection has a vital impact on the analysis and interpretation of visual scenes. It is widely utilized in various fields, including healthcare, autonomous driving, and vehicle surveillance.
Sohaya El Hamdouni, Boutaina Hdioud, Sanaa El Fkihi   +2 more
doaj   +1 more source

RGBD Salient Object Detection, Based on Specific Object Imaging

Sensors, 2022
RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects.
Xiaolian Liao, Jun Li, Leyi Li, Caoxi Shangguan, Shaoyan Huang   +4 more
doaj   +1 more source

Relation Networks for Object Detection

, 2018
Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era.
Dai, Jifeng, Gu, Jiayuan, Hu, Han, Wei, Yichen, Zhang, Zheng   +4 more
core   +1 more source

An Adaptive Management Model for Brown Bears in Hokkaido: Based on Total Population and the Number of Nuisance Bears

Population Ecology, EarlyView.
Bear management changes management actions according to the horizontal axis of the population size and the vertical axis of the number of nuisance bears. Aiming for the target population size of Ntar, Actions I and II protect the bears, and Action IV reduces the population.
Hiroyuki Matsuda, Akira Maki, Tsutomu Mano, Hifumi Tsuruga, Hino Takafumi, Shinichi Horino   +5 more
wiley   +1 more source

A deep convolutional neural network for diabetic retinopathy detection via mining local and long‐range dependence

CAAI Transactions on Intelligence Technology
Diabetic retinopathy (DR), the main cause of irreversible blindness, is one of the most common complications of diabetes. At present, deep convolutional neural networks have achieved promising performance in automatic DR detection tasks.
Xiaoling Luo, Wei Wang, Yong Xu, Zhihui Lai, Xiaopeng Jin, Bob Zhang, David Zhang   +6 more
doaj   +1 more source

Auxiliary Detection Head for One-Stage Object Detection

IEEE Access, 2020
The auxiliary classifier can improve the performance of classification networks. However, the utility of the auxiliary detection head has not been explored in the object detection field.
Guozheng Jin, Rin-Ichiro Taniguchi, Fengzhong Qu   +2 more
doaj   +1 more source

Deep Regionlets for Object Detection

, 2018
In this paper, we propose a novel object detection framework named "Deep Regionlets" by establishing a bridge between deep neural networks and conventional detection schema for accurate generic object detection.
Kaiming He, M Everingham, PF Felzenszwalb, S Mallat, S Ren, T Ahonen, Tsung-Yi Lin, W Ouyang, Wei Liu, X Wang   +9 more
core   +1 more source

FoveaBox: Beyond Anchor-based Object Detector

, 2020
We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search ...
Jiang, Yuning, Kong, Tao, Li, Lei, Liu, Huaping, Shi, Jianbo, Sun, Fuchun   +5 more
core   +1 more source

Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection

Expert Systems with Applications
Achieving joint learning of Salient Object Detection (SOD) and Camouflaged Object Detection (COD) is extremely challenging due to their distinct object characteristics, i.e., saliency and camouflage. The only preliminary research treats them as two contradictory tasks, training models on large-scale labeled data alternately for each task and assessing ...
Yi Liu, Chengxin Li, Xiaohui Dong, Lei Li, Dingwen Zhang, Shoukun Xu, Jungong Han   +6 more
openaire   +2 more sources