Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques

Custom object detection plays a vital role in computer vision applications. However, developing an accurate and efficient custom object detector requires a substantial amount of labeled training data and significant computational resources. In this research, we propose a custom object detection framework that leverages transfer learning with pre-trained models to improve detection tech-niques.The framework first utilizes a pre-trained deep learning model, such as ResNet or VGGNet, as a feature extractor. The pre-trained model is trained on a large-scale dataset, enabling it to learn high-level features from various objects. By reusing the pre-trained model's convolutional layers, we effectively capture generic features that can be transferred to the custom object detection task.Experimental evaluations on benchmark datasets demonstrate the effectiveness of our ap-proach. The custom object detector achieved superior detection performance compared to tradi-tional methods, especially when the target objects have limited training data. Additionally, our framework significantly reduces the amount of training time and computational resources required, as it leverages pre-trained models as a starting point.