Deep Learning In Object Detection

This Special Issue provides an overview of the recent advancements and emerging trends in deep learning for object detection. Deep-learning-based approaches have demonstrated significant success in accurately localizing and classifying objects within complex and diverse scenes, leveraging convolutional neural networks (CNNs) to understand rich representations of objects from raw image data. The Special Issue encompasses a wide range of research directions, focusing on the development of novel architectures, feature extraction techniques, and training methodologies for deep-learning-based object detection. Attention mechanisms, such as self-attention and spatial attention, have been explored to improve the localization and recognition of objects, while contextual information and semantic relationships between objects have been incorporated to enhance detection performance.