: Faster R-CNN

Faster R-CNN with Equilibrium Optimizer for Real-Time Object Detection and Classification Framework to Aid Visually Impaired People

In general, blind or Visually Impaired People (VIP) survive their lives in hardship, with the main challenge that they are incapable of living entirely independently. Object detection is a major contribution of computer vision and Machine Learning (ML) in the detection of objects in videos or images. Numerous research...

Intelligent Visual Recognition for Real-Time Object and Color Detection

Object and color detection is an important part of visual recognition and scene understanding, because recognizing an object together with its color provides the contextual cues needed to interpret real-world situations. Accurate detection is difficult, however, because of changes in lighting, complex backgrounds, and...

A Hybrid Deep Learning Architecture for Image Classification Across Diverse Visual Recognition Applications

This study trains a hybrid deep learning system to efficiently recognize photos using the Caltech-256 dataset and findings validate the proposed hybrid design's provision of a strong and efficient model.

A Comparative Analysis of Transformer-based and CNN-based Object Detection Models on Low Light Images

Single-stage object detection in the deep learning era: a critical survey of CNN-, transformer-, and hybrid architectures

This survey provides a critical review of single-stage detectors developed between 2016 and 2026, structured around three architectural axes: the convolutional family, the Transformer family, and hybrid CNN–Transformer designs.

Face Mask Detection Using Deep Learning: A Comparative Study of CNN, VGG16 and MobileNetV2 for Real-Time Applications

A comparative study of three deep learning architectures, namely a Custom Convolutional Neural Network (CNN), VGG16, and MobileNetV2, for face mask detection indicates that lightweight transfer learning models offer an effective and practical solution for real-time face mask detection in resource-constrained environmen...