A Novel Dynamic Confidence Threshold Estimation AI Algorithm for Enhanced Object Detection

Object detection (OD) is a significant component in the field of computer vision applications, ranging from autonomous vehicles to ensuring security in Department of Defense (DoD) systems. Despite the huge progress that has been made, the accuracy of the models in various situations is limited to static detection parameters like confidence threshold and NMS threshold. The static method of deciding the thresholds may lessen the effect of detections, thereby impacting the mean average precision (mAP) score and the overall efficacy of OD models in real-world scenarios. To overcome this drawback, this study introduced a novel approach that dynamically adjusts the confidence threshold and NMS threshold based on statistical measures of the entire object representation in a data plane. The COCO Dataset was chosen to evaluate the performance of dynamic threshold techniques across five different pre-trained state-of-the-art models like YOLO v5, Faster R-CNN, FCOS, RetinaNet, and SSD300. Among all the models, Faster R-CNN outperformed others with a mAP of 56.11 %. This dynamic approach makes the OD models more reliable by improving the adaptability and robustness of object detection models.