Optimizing Object Detection in YOLOv5 using Adaptive Genetic Algorithm: A Study on Population Sizes for Enhanced Accuracy

In recent years, deep learning-based object detection models like YOLOv5 have demonstrated remarkable efficiency in accurately identifying objects within images. However, achieving optimal performance still presents challenges. Many researchers have employed hybrid models to enhance the mean average precision (mAP) in the YOLOv5 architecture. However, only a few have explored hyperparameter-based optimization techniques to improve mAP. We use an adaptive genetic algorithm-based approach for analyzing Arecanut X-ray images to improve detection accuracy within the YOLOv5 framework. Balancing population size and computational efficiency is crucial for effective exploration and exploitation of the solution space in genetic algorithms without incurring unnecessary computational costs. This research investigates the influence of various population sizes on mAP using a genetic algorithm. Our findings indicate that a population size of 50 is ideal for achieving the best mAP among the explored sizes, when evolved for 300 generations.