Description
YOLOv4 is a one-stage object detection model that improves on YOLOv3 with several bags of tricks and modules introduced in the literature. The components section below details the tricks and modules used.
Papers Using This Method
Pattern-Based Phase-Separation of Tracer and Dispersed Phase Particles in Two-Phase Defocusing Particle Tracking Velocimetry2025-06-22Event-Based Crossing Dataset (EBCD)2025-03-21WalnutData: A UAV Remote Sensing Dataset of Green Walnuts and Model Evaluation2025-02-27YOLOv4: A Breakthrough in Real-Time Object Detection2025-02-06Vision-Integrated LLMs for Autonomous Driving Assistance : Human Performance Comparison and Trust Evaluation2025-02-06SPFFNet: Strip Perception and Feature Fusion Spatial Pyramid Pooling for Fabric Defect Detection2025-02-03Efficient Object Detection of Marine Debris using Pruned YOLO Model2025-01-27Object Detection Approaches to Identifying Hand Images with High Forensic Values2024-12-21Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)2024-12-19UICE-MIRNet guided image enhancement for underwater object detection2024-09-24YOLO-Former: YOLO Shakes Hand With ViT2024-01-11HyperSense: Hyperdimensional Intelligent Sensing for Energy-Efficient Sparse Data Processing2024-01-04Toward Improving Robustness of Object Detectors Against Domain Shift2023-12-02YOLOv5s-BC: An improved YOLOv5s-based method for real-time apple detection2023-11-10Application of deep learning for livestock behaviour recognition: A systematic literature review2023-10-20Parking Spot Classification based on surround view camera system2023-10-05Randomize to Generalize: Domain Randomization for Runway FOD Detection2023-09-23Automatic Signboard Recognition in Low Quality Night Images2023-08-17YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems2023-07-26Group channel pruning and spatial attention distilling for object detection2023-06-02