mAPLoss: Enhancing Directional Accuracy in Optimization for Dense Object Detectors

Object detection is a fundamental task in computer vision and supports more complex tasks, such as object tracking and trajectory prediction. Despite significant advancements in deep learning, traditional loss functions often optimize classification and localization independently, requiring manual tuning of multiple weights. This approach can lead to misalignment with mean average precision (mAP). In this article, we propose a novel mAP-based loss function that directly correlates with the mAP metric. Our contributions include a novel perspective on designing loss functions for object detection, a new strategy for assigning positive and negative samples, and the establishment of a new state-of-the-art approach for loss function design. Our approach improves both interpretability and performance, effectively overcoming the limitations of conventional methods that rely on manually designed weights. Extensive experiments on standard benchmarks demonstrate that our method consistently outperforms existing State-of-the-Art techniques.