Enhancing wildlife monitoring with computer vision: A dataset for automated detection of barbary macaques.

Object-guided multi-granularity unsupervised hashing for image retrieval.

Automatic generation of synthetic data and object detection datasets in virtual environments based on tomato-harvesting robot vision

The rapid advancement of deep learning and computer vision technologies is transforming sports analytics, enabling more precise performance analysis and motion tracking. However, accurately estimating hammer throw distances without physical measurements remains a challenge due to the complexity of the motion and the high speed of the projectile. The ability to accurately predict hammer throw distances would be particularly useful in indoor training settings, where the hammer’s trajectory is stopped by safety nets or mattresses from a short distance. To address this challenge, we introduce a deep learning-based method that combines object detection with physics-based modeling to estimate motion outcomes. Our methodology employs a dual-camera setup to capture side and back views of the throw, applies advanced object detection to track the hammer’s position frame by frame, and reconstructs 3D trajectory points to estimate the release speed, angle, and height that allow predicting the throw distance. By enabling quantitative assessment of performance without relying on physical landing measurements, the proposed approach supports objective training feedback on the release parameters and distance estimation in typical training environments where traditional distance-based evaluation is not feasible. Our approach enables accurate performance evaluation in spatially constrained settings. Experimental results demonstrate that our approach achieves an average error of less than three meters ( ∼ 4 %) in estimating the distances compared to ground truth measurements. Our codes and trained models will be made publicly available once the paper is published at https://github.com/AhmedEH28/Hammer-Throw-Distance-Estimation .

A Large-Scale Longitudinal Dataset for Pig Tracking and Re-Identification

Individual detection is a fundamental task for biomass estimation, animal welfare, preliminary disease diagnosis, and intelligent rearing in silkworm culture. However, due to the small size of individuals, occlusion, high density, and complex backgrounds, traditional object detection methods fail to capture global representations, resulting in limited detection performance. To address this issue, we propose a novel method for silkworm individual detection using the You Only Look Once version 5 (YOLOv5) network and a self-attention module. Specifically, multiple local silkworm images were cropped from original images collected using a mobile phone, and labeled using the LabelImg toolkit. The head and tail were used as detection targets to address the challenges of occlusion and high density. A hybrid module was designed to simultaneously extract global and local features by bridging convolutional operations and the self-attention mechanism. An effective detector, termed Hybrid YOLO, was developed by embedding the hybrid module into YOLOv5s. Experimental results demonstrated that the proposed detector achieved a Recall of 91.44% and 81.25%, a Precision of 89.31% and 89.51%, an F1-score of 0.90 and 0.85, and an AP (Average Precision) of 92.87% and 88.98% for the head and tail targets, respectively. The mAP (mean Average Precision) of Hybrid YOLO reached 90.93%, outperforming original YOLOv5 variants, state-of-the-art models, and small target detection networks. This study provides theoretical insights and technical support for future research.

DarkCORE: Efficient low-light object detection via collaborative reflectance denoising and object-oriented feature enhancement

IRDFusion: Iterative relation-map difference guided feature fusion for multispectral object detection

Intelligent monitoring of coastal outfalls via multi-source remote sensing image fusion.

Depth correction and edge guidance network for RGB-D salient object detection

CDFNet: Cross-dimension fusion network with dual feature enhancement for multimodal object detection

Dual-teacher fusion with augmented branch for semi-supervised object detection

Fusing dynamic scene-aware augmentation with adaptive matching reweighting for object detection

Development of a computer vision-aided lameness detection system for dairy cattle

VISTA-3D : Training-free unfolding for vision-based 3D object detection.

Learning modality knowledge with proxy for RGB-Infrared object detection

Illumination-adaptive feature enhancement for low-light object detection

Dual feature masking stage-wise knowledge distillation for object detection

MPCANet: Multi-physical prior guided cross-modal attention and fusion network for RGB-T salient object detection

Toward convenient depression detection using two non-hair-bearing frontal EEG channels.