IoU Loss

IoU (Intersection over Union) loss functions are crucial for training object detection models, aiming to optimize the overlap between predicted and ground-truth bounding boxes. Recent research focuses on improving IoU loss by addressing limitations such as its plateauing behavior and inconsistency with evaluation metrics, leading to the development of variations like Unified-IoU and Inner-IoU, which incorporate additional factors or auxiliary boxes to enhance training efficiency and generalization. These advancements contribute to improved accuracy in object detection tasks across various domains, including image and document processing, particularly at higher IoU thresholds where precision is paramount. The resulting improvements in bounding box regression directly impact the performance of object detection systems in diverse applications.