Detection Network

Detection networks, a core component of computer vision and signal processing, aim to accurately identify and locate objects or events within data. Current research emphasizes improving robustness against adversarial attacks and noise, particularly for small or low-contrast objects, often employing architectures like YOLO and its variants, transformers, and convolutional neural networks, sometimes incorporating techniques like attention mechanisms and multi-scale feature fusion. These advancements have significant implications for diverse applications, including medical imaging (e.g., lesion detection), autonomous driving (e.g., obstacle detection), and remote sensing (e.g., underground mapping), improving accuracy and efficiency in these fields. Furthermore, research explores semi-supervised and unsupervised learning methods to address data scarcity challenges.