Results 181 to 190 of about 19,033 (238)

A dual-modal vision system for non-invasive real-time monitoring of broiler diarrhea under low-light conditions. [PDF]

Poult Sci
Zhang W, Sun J, Sun J, Yang K, Xie X, Chen C.   +5 more
europepmc   +1 more source

High-precision pothole detection using the ECC-YOLO network with deformable convolution and attention mechanisms. [PDF]

Sci Rep
Li H, Zhang C, Ye S.
europepmc   +1 more source

A Bio-Inspired Lightweight Human Action Recognition Method Based on Human Keypoint Detection. [PDF]

Biomimetics (Basel)
Huang W, Wu M, Chen W, Zhou Q.
europepmc   +1 more source

Real-time peach detection method in complex environments based on improved YOLOv8 and multi-attention fusion. [PDF]

Front Plant Sci
You S, Li J, Yao L, Yan C, Wu Z.
europepmc   +1 more source

Safety compliance monitoring for oil tank unloading based on multimodal feature fusion and knowledge reasoning. [PDF]

Sci Rep
Liu T, Sun C, Wang B, Yao L.
europepmc   +1 more source

YOLO-DCF: dual distillation and context-aware fusion for defect detection. [PDF]

Sci Rep
Xing H, Yang Z.
europepmc   +1 more source

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YOLO-F: YOLO for Flame Detection

International Journal of Pattern Recognition and Artificial Intelligence, 2023
Flame detection is of great significance in a fire prevention system. YOLOv4 has poor real-time performance on flame detection caused by the complex structure and high parameter size. To address this problem, a novel flame detection framework, YOLO for flame (YOLO-F), is proposed in this paper. The backbone of YOLOv4 is simplified from the original 53
Kun Xu, Yuan Xu, Yuanxin Xing, Zhanwen Liu   +3 more
openaire   +1 more source

BAFPN: An Optimization for YOLO

2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021
Object detection is essential in Computer Vision and is widely applied in all areas. This paper proposes a method called BAFPN. BAFPN is a new bidirectional Feature Pyramid Network that constructs accurate object detection networks based on YOLOv4 by implementing Adaptively Spatial Feature Fusion.
Hehe Li, Xin Li, Lianfeng Li, Junbin Pan, Shujia Zhu, Jinwei Du, Peng Wang   +6 more
openaire   +1 more source

YOLO*C — Adding context improves YOLO performance

Neurocomputing, 2023
You Only Look Once (YOLO) algorithms deliver state-of-the-art performance in object detection. This research proposes a novel one-stage YOLO-based algorithm that explicitly models the spatial context inherent in traffic scenes. The new YOLO*C algorithm introduces the MCTX context module and integrates loss function changes, effectively leveraging rich ...
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YOLO-FD: YOLO for Face Detection

2019
Face detection is a fundamental step for any face analysis approach. However, it remains as an unsolved problem in computer vision, specially, when it comes to the variability and distractions of in-the-wild environments. Moreover, a face detector must be accurate and fast to be used in surveillance/biometrics scenarios.
Luan P. e Silva, Júlio C. Batista, Olga R. P. Bellon, Luciano Silva   +3 more
openaire   +1 more source