Not long ago, spotting an AI-generated image felt almost easy. The internet circulated a familiar checklist: count the fingers, look ...
1 Department of Computer and Instructional Technologies Education, Gazi Faculty of Education, Gazi University, Ankara, Türkiye. 2 Department of Forensic Informatics, Institute of Informatics, Gazi ...
In the task of ship detection, convolutional neural networks (CNNs) based on deep learning have achieved remarkable progress. However, two-stage object detectors often overlook critical distinctions ...
With the development of Industry 4.0, there is increasing emphasis on automating assembly tasks traditionally performed manually by skilled workers [1]. These tasks often involve fasteners, such as ...
Abstract: Object detection in 360-degree camera systems presents unique challenges due to extreme resolutions, distortions, occlusions, and scale variation. This paper proposes Refined Faster R-CNN ...
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 ...
Abstract: Real-time and robust object detection is a critical component of autonomous vehicle perception systems, directly impacting operational safety and decision making. This paper proposes a ...
Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment. Conventional computer-aided ...
Abstract: This paper introduces a CNN-RNN hybrid architecture of edge-optimized system to detect and track real-time objects in self-driving cars. The architecture combines convolutional neural ...
Abstract: This study addressed the modernization of traditional coconut maturity assessment methods by developing an Android-based mobile application integrated with a machine learning model. The ...