Tag: object detection

Researchers have developed an occlusion-aware 3D Gaussian splatting framework that reduces visual artifacts and improves reconstruction consistency for crowdsourced heritage ...

A systematic review of 44 YOLO-based railway track defect detection studies finds that none demonstrates fully operational deployment, with all ...

Researchers evaluating text-to-image generative models found that automated tools struggle to reliably measure subtle gender-object stereotypes, often confounded by significant ...

Researchers developed SFMDet, an augmented YOLOv12-based framework combining state-space scanning and cross-attention to improve object detection across cluttered construction environments.

Researchers developed the CFS-DETR framework to improve agglomeration detection in fiber ultrastructure scanning electron microscopy across shifting scales.

Researchers report that their enhanced YOLOv8n model, MFF-YOLO, improves SAR ship detection accuracy on two benchmark datasets by combining multi-scale ...

A systematic review of 60 strawberry detection studies finds that 35 percent applied data augmentation before dataset splitting, a controlled ...

A landmark head-to-head study finds that a YOLOv3 image detector outperforms point-cloud-native and modular machine learning methods for detecting moving ...

Researchers built a dual-stream deep learning framework that localizes and classifies facial emotions and binary gender labels together, achieving 94.56% ...

A benchmarked vision pipeline converts ceiling-mounted food-production video into zone occupancy, entries, exits and dwell-time variables for a manufacturing digital ...