Bounding Box Regression (BBR) plays a critical role in object detection by refining the predicted location and size of objects to enhance model accuracy. This process involves adjusting the coordinates of the proposed bounding boxes to enhance their precision. The Intersection over Union (IoU) loss metric was introduced to improve the IoU metric for integration into the model training process, measure discrepancies between the model's predictions and ground truth, and ensures meaningful gradient updates during training. In practice, IoU loss has demonstrated improvements in object detection performance by enhancing the localization accuracy of bounding boxes. Despite significant technological advancements and the various advantages and disadvantages of IoU loss, improving the accuracy and efficiency of BBR remains an active research area in computer vision. Various IoU loss variations have evolved with new formulations and methods to improve accuracy and convergence speed. A new loss function, Dimensional Angle Precision IoU (DAPIoU) loss, is introduced in this research to enhance BBR and serve as a new object detection loss function to address the limitations in previous loss function research results. This study conducts three types of experiments: single-group BBR simulation experiment on synthetic data, simulation experiment on synthetic data, and experiment on real-world datasets. The datasets used are MS-COCO and PASCAL VOC datasets. The object detection models used are YOLOv7, YOLOv9, and Faster R-CNN. The results from the real-world datasets experiments are evaluated using the mean Average Precision (mAP) method, including object size metrics, comparing several previous loss functions based on IoU. © 2013 IEEE.