Enhancing Object Detection Techniques Through Transfer Learning and Pre-trained Models

Abstract

This study aims to enhance object detection systems by comparing pre-trained classification models with custom-trained ones, focusing on task-based deep learning for image recognition. The problem addressed is the challenge of accurately detecting and classifying objects in complex environments where traditional recognition systems may fall short. The proposed solution leverages transfer learning utilizing pre-trained models like ResNet or VGGNet as feature extractors. By exploiting the convolutional layers of these models, the system captures common features for specific detection tasks. Experimental analyses on benchmark datasets confirm the efficacy of this approach, demonstrating improved detection accuracy and efficiency in various scenarios. Specifically, FasterRCNN achieves a mean Average Precision (mAP) of 78% on synthetic datasets and 74% on real datasets at an Intersection over Union (IoU) threshold of 0.5. This indicates FasterRCNN's superior performance in terms of accuracy, making it a strong candidate for applications requiring high detection accuracy.