IEEE

Mask R-CNN with HRNet Backbone for Person Detection Evaluation on COCO Using mIoU and Threshold Optimization

Abstract: A crucial computer vision problem is person detection, but real-world challenges including crowded backgrounds, inconsistent lighting, and object occlusion require accurate and robust models ...
IEEE

Removal Then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection

Abstract: In recent years, object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention and has been widely implemented across a diverse array of ...
IEEE

Lightweight Multiscale Feature Fusion and Multireceptive Field Feature Enhancement for Small Object Detection in the Aerial Images

Abstract: Small uncrewed autonmous vehicles (UAVs) equipped with deep learning models are increasingly used to detect small objects both on the ground and in aerial environments. Since small objects ...
IEEE

Salient Object Detection of Dynamic Night Scenes via Bio-Inspired Spotlight Attention and Hierarchical Edge-Texture Fusion

Abstract: The perception of night scenes is of crucial importance for driving safety. In the dimly lit night environment, as the visibility of objects decreases, both experienced and inexperienced ...
IEEE

Adaptive Grid Selection Training Strategy for Tiny Object Detection

Abstract: Detecting small objects and managing occlusions remain persistent challenges in object detection tasks, particularly in complex scenarios with diverse environments or densely packed scenes.
IEEE

A Patch-Based CNN Approach for Object Detection Using RGB and NIR Image Fusion

Abstract: This study explores the fusion of RGB and NearInfrared (NIR) images for object detection. Three fusion techniques, such as RedSwap, Heatmap, and the proposed NIRR Difference, were evaluated ...
IEEE

Spatial-Frequency Collaborative Learning for Camouflaged Object Detection

Abstract: Camouflaged object detection (COD) is a challenging task that struggles to accurately detect the objects concealed in the surrounding environment. This is largely attributed to the intrinsic ...
IEEE

Ensemble of Fast R-CNN with Bi-LSTM for Object Detection

Abstract: Object detection is a critical task in computer vision, with applications ranging from autonomous driving to medical imaging. Traditional object detection models, such as Fast R-CNN, have ...
IEEE

Adaptive Receptive Field Network for Small Objects Detection in Remote Sensing Images

Abstract: Small object detection in remote sensing images is severely hampered by the significant scale variation even among small objects. Conventional methods often rely on a static receptive field ...
IEEE

TRAM: Transformer-Based Mask R-CNN Framework for Underwater Object Detection in Side-Scan Sonar Data

Abstract: Accurate detection and segmentation of underwater objects in side-scan sonar (SSS) imagery remain challenging due to noise, cluttered backgrounds, and low-contrast conditions. In this paper, ...
GitHub

GitHub - longph3105/rf_detr: RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO and designed for fine-tuning.

RF-DETR is the first real-time model to exceed 60 AP on the Microsoft COCO object detection benchmark alongside competitive performance at base sizes. It also achieves state-of-the-art performance on ...
IEEE

MSTCNet: Multiscale Transformer-CNN Network for Robotic Grasp Detection

Abstract: Existing robotic grasp detection methods often struggle with inaccurate predictions in complex scenarios involving multiple objects and textured backgrounds. Most existing methods attempt to ...