Automated Image Captioning Using Deep Learning - Avinash P., Sai Prasath P., Jennet Shinny D., Keshav Adhitya M., Chandra B. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Object detection, pivotal in computer vision, spans diverse applications like autonomous driving, medical imaging, etc. Deep learning, notably, enhances detection by hierarchically representing data. Two prevalent approaches are region proposal-based (e.g., R-CNN, Fast R-CNN) and unified pipeline-based (e.g., YOLOv2). The latter, exemplified by YOLOv2, emphasizes speed and simplicity. Innovations like batch normalization and anchor boxes refine accuracy. Variants like real-time YOLO adapt for specific platforms (e.g., Non-GPU computers), while methods like SSD and DSSD optimize speed and accuracy trade-offs. Recent advancements include YOLOv3's binary cross-entropy loss for improved small object detection