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J N Rajeshkumar is an Assistant Professor in the Department of Computer Science and Engineering at Sree Sastha Institute of Engineering and Technology, India. His contribution focuses on deep learning, image processing, and computer vision.
Deep learning has revolutionised image processing and computer vision by allowing machines to autonomously acquire hierarchical representations from extensive visual datasets. In contrast to traditional image-processing methods that rely significantly on handcrafted characteristics, deep learning frameworks are capable of autonomously extracting pertinent spatial, semantic, and contextual features from unprocessed images. This chapter elucidates the essential principles of deep learning pertinent to image analysis and examines prominent architectures such as Convolutional Neural Networks (CNNs), AlexNet, VGG, GoogLeNet/Inception, ResNet, DenseNet, MobileNet, EfficientNet, U-Net, Generative Adversarial Networks (GANs), Vision Transformers (ViTs), and hybrid CNN-transformer models. Applications in healthcare, manufacturing, agriculture, autonomous transport, remote sensing, security, robotics, and infrastructure oversight are likewise discussed. The chapter ultimately explores difficulties with computing demands, data reliance, model interpretability, resilience, domain adaption, privacy, and implementation, concluding with prospective research avenues in efficient, explainable, multimodal, and reliable computer vision systems.