An improved deep learning-based optimal object detection system from images

• Published in: Multimedia tools and applications Vol. 83; no. 10; pp. 30045 - 30072
• Main Authors: Yadav, Satya Prakash, Jindal, Muskan, Rani, Preeti, de Albuquerque, Victor Hugo C., dos Santos Nascimento, Caio, Kumar, Manoj
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Automation; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Deep learning; Digital imaging; Human error; Identification; Image processing; Image quality; Machine learning; Multimedia; Multimedia Information Systems; Neural networks; Object recognition; Pattern recognition; Sensors; Special Purpose and Application-Based Systems; Telematics; Chess Piece Identification; Object Detection; Single Stage Detector (SSD); You Only Look Once (YOLO); Faster Region-Based Convolutional Neural Networks (R-CNN)
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-16736-5

Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.