RefDiffNet is a lightweight input enhancement block that uses reference image comparison to expose PCB defects, delivering up to 18% relative mAP50:95 gains across YOLO, RT-DETR, and Faster R-CNN detectors with 0.004-0.005M extra parameters.
YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness
8 Pith papers cite this work, alongside 57 external citations. Polarity classification is still indexing.
8
Pith papers citing it
57
external citations · external index
representative citing papers
A new leaf-instance dataset for soybean-cotton detection and segmentation collected across growth stages and conditions from commercial farms is presented and validated with YOLOv11.
FSDC-DETR improves small-object AP by 6.8–6.9 points on VisDrone and AITODv2 by explicit frequency-spatial fusion and wavelet-style downsampling inside a DETR hybrid encoder.
GEAR-Seg decouples segmentation, semantic description, and LLM reasoning into an explicit chain for interpretable zero-shot reasoning segmentation while generating the GEAR-131K dataset.
Introduces the Attention-Aware Pipeline (Capture-Record-Revisualize) to surface design tensions in XR attention feedback systems, illustrated via three prototypes and a musician eye-tracking study.
WSA-Net uses partial convolutions, heterogeneous grouping attention, geometric reconstruction, and context anchoring to enhance low-SCR hyperbolic signatures in GPR data, reaching 0.6958 mAP@0.5 at 164 FPS with 2.412M parameters on the RTST dataset.
YOLOv8 trained on 7,959 images (public guns/knives plus custom blunt objects) for real-time detection of three threat classes in Indian surveillance scenarios.
citing papers explorer
-
RefDiffNet: Learning to Expose Subtle PCB Defects Before Detection
RefDiffNet is a lightweight input enhancement block that uses reference image comparison to expose PCB defects, delivering up to 18% relative mAP50:95 gains across YOLO, RT-DETR, and Faster R-CNN detectors with 0.004-0.005M extra parameters.
-
A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation
A new leaf-instance dataset for soybean-cotton detection and segmentation collected across growth stages and conditions from commercial farms is presented and validated with YOLOv11.
-
FSDC-DETR: A Frequency-Spatial Domain Collaborative DETR for Small Object Detection
FSDC-DETR improves small-object AP by 6.8–6.9 points on VisDrone and AITODv2 by explicit frequency-spatial fusion and wavelet-style downsampling inside a DETR hybrid encoder.
-
GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine
GEAR-Seg decouples segmentation, semantic description, and LLM reasoning into an explicit chain for interpretable zero-shot reasoning segmentation while generating the GEAR-131K dataset.
-
The Attention-Aware Pipeline: Design Tensions from Making Attention Visible in XR
Introduces the Attention-Aware Pipeline (Capture-Record-Revisualize) to surface design tensions in XR attention feedback systems, illustrated via three prototypes and a musician eye-tracking study.
-
A Weak-Signal-Aware Framework for Subsurface Defect Detection: Mechanisms for Enhancing Low-SCR Hyperbolic Signatures
WSA-Net uses partial convolutions, heterogeneous grouping attention, geometric reconstruction, and context anchoring to enhance low-SCR hyperbolic signatures in GPR data, reaching 0.6958 mAP@0.5 at 164 FPS with 2.412M parameters on the RTST dataset.
-
Real-Time Threat Detection from Surveillance Cameras using Machine Learning
YOLOv8 trained on 7,959 images (public guns/knives plus custom blunt objects) for real-time detection of three threat classes in Indian surveillance scenarios.
- Train, Test, Re-evaluate: Schedule-Sensitive Evaluation of Generative Data for Hand Detection