For small object detection, I'd recommend training at 1280 resolution with YOLOv5s or YOLOv5m as the best balance between accuracy and speed - smaller models at higher resolution often outperform ...

Weakly supervised object detection (WSOD) is a task that uses only image-level category labels to train an object detector. The most common weakly supervised object detection framework uses ‘argmax’ ...

Introduced in the paper "Roboflow 100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language Models", RF100-VL is a large-scale collection of 100 multi-modal datasets with diverse concepts ...

Additionally, underwater environments present unique challenges for object detection, such as background interference, dense object distribution, and occlusion. These issues contrast sharply with ...

In comparison to methods based on stereo cameras and lidar, monocular 3D object detection offers advantages such as a broad detection field and low deployment costs. However, the accuracy of existing ...

This paper addresses the challenge of detecting Out-of-Distribution (OOD) objects in autonomous vehicles, focusing on identifying and localizing objects absent from the training data. We propose a ...

Most significant improvements were observed in small object detection, where SOTA achieved an AP gain of 1.5. For O2M comparison, DIEM models achieved slightly higher AP than YOLO, which is the ...

Learn how to set up YOLO object detection on Raspberry Pi AI HAT. Step-by-step guide for hardware, software, and real-world applications.