Deep Learning Based Object Detection

Deep learning-based object detection aims to automatically identify and locate objects within images or videos, leveraging the power of neural networks to surpass traditional methods. Current research emphasizes improving robustness in challenging conditions like adverse weather, heavy occlusion, and domain shifts, often employing architectures such as YOLO, Faster R-CNN, and variations thereof, along with techniques like domain adaptation and semi-supervised learning to address data limitations. This field is crucial for diverse applications, ranging from biomedical image analysis and autonomous driving to industrial automation and environmental monitoring, where accurate and reliable object detection is paramount.