Optimizing RetinaNet anchors using differential evolution for improved object detection.

Object detection is a fundamental task in computer vision. It has two primary types: one-stage detectors known for their high speed and efficiency, and two-stage detectors, which offer higher accuracy but are often slower due to their complex architecture. Balancing these two aspects has been a significant challenge in the field. RetinaNet, a premier single-stage object detector, is renowned for its remarkable balance between speed and accuracy. Its success is largely due to the groundbreaking focal loss function, which adeptly addresses the issue of class imbalance prevalent in object detection tasks. This innovative approach significantly enhances detection accuracy while maintaining high speed, making RetinaNet an ideal choice for a wide range of real-world applications. However, its performance decreases when applied to datasets containing objects with unique characteristics, such as objects with elongated or squat shapes. In such cases, the default anchor parameters may not fully meet the requirements of these specialized objects. To overcome this limitation, we present an enhancement to the RetinaNet model to improve its ability to handle variations in objects across different domains. Specifically, we propose an optimization algorithm based on Differential Evolution (DE) that adjusts anchor scales and ratios while determining the most appropriate number of these parameters for each dataset based on the annotated data. Through extensive experiments on datasets spanning diverse domains such as the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), the Unconstrained Face Detection Dataset (UFDD), the TomatoPlantFactoryDataset, and the widely used Common Objects in Context (COCO) 2017 benchmark, we demonstrate that our proposed method significantly outperforms both the original RetinaNet and anchor-free methods by a considerable margin.