Abstract: Despite detection Transformer (DETR)-like methods having improved end-to-end detection capabilities, they fundamentally struggle with the uniform processing of entire flattened feature maps, ...
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 ...
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, ...
Abstract: Space noncooperative object detection (SNCOD) is an essential part of space situation awareness. The localization and segmentation capabilities of the salient object detection (SOD) method ...
Abstract: To address the problems of relying on electronic repositories and being vulnerable to network influence in obtaining key information of literature in mainstream literature management ...
Abstract: Camouflaged object detection (COD) is challenging for both human and computer vision, as targets often blend into the background by sharing similar color, texture, or shape. While many ...
Abstract: Small object detection in uncrewed aerial vehicle (UAV) images is one of the critical aspects for its widespread application. However, due to limited feature extraction for small objects and ...
Description Tensorflow Object Detection API is the easy to use framework for creating a custom deep learning model that solves object detection problems. If you already have your own dataset, you can ...
Abstract: In autonomous driving, understanding the surroundings is crucial for safety. Since most object detection systems are designed to identify known objects, they may miss unknown or novel ...
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