Performance Analysis of Different AIML Techniques for Image Annotation in Object Detection

Abstract

Image annotation plays a crucial role in computer vision by facilitating the training and development of accurate object detection models. However, the conventional manual annotation process is time-consuming and labor-intensive, prompting the exploration of automated techniques. This research paper focuses on the application of Artificial Intelligence and Machine Learning (AIML) techniques for image annotation, specifically in the context of object detection. In this we evaluate and compare the effectiveness of various AIML techniques, including deep learning-based approaches such as Convolutional neural networks (CNNs), Recurrent neural networks (RNNs), and Generative adversarial networks (GANs). To conduct this evaluation, we utilize the KITTI dataset, a widely used benchmark dataset in the field of computer vision. To assess the performance of the different models, we employ standard evaluation metrics such as precision, recall etc,. These metrics provide insights into the accuracy and consistency of the annotations generated by the models. The findings of this study are expected to contribute to the development of more efficient and accurate object detection systems. By identifying the most effective AIML techniques for image annotation, researchers and practitioners can enhance the capabilities of computer vision applications in fields such as autonomous driving, surveillance, and image understanding. These advancements have the potential to revolutionize industries and improve the overall performance and reliability of computer vision systems.