{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/detectron2-object-detection-manipulating","title":"Detectron2 Object Detection & Manipulating Images using Cartoonization","arxiv_id":null,"date":"2021-08-01","proceeding":"International Journal of Engineering Research & Technology (IJERT) 2021 8","authors":["Allena Venkata Sai Abhishek","Sonali Kotni"],"abstract":"In today's world, there is a rapid increase in the autonomous vehicle. There are various levels of autonomous vehicles depending upon the degree of autonomy-for the lower degree of autonomy driver has more power and functionality for managing, on coming to the fully automated vehicle like Tesla are expected to have full control over the functions. These advances cooperate to plan the vehicle's position and its nearness to everything around it. Because of this, there is popularity for these vehicles, since they give a great deal of advantages to individuals utilizing them. We use the Facebook AI Research software system that implements object detection algorithms, Caffe2 deep learning framework for advanced object detection by offering speedy training. We have also manipulated images to derive insights addressing the issues companies face when making the step from research to production. We have implemented detectron2 object detection for faster detection of objects. There is labeling of the object & we used manipulation of images using cartoonization.","url_abs":"https://www.ijert.org/detectron2-object-detection-manipulating-images-using-cartoonization","url_pdf":"https://www.ijert.org/research/detectron2-object-detection-manipulating-images-using-cartoonization-IJERTV10IS080122.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"detectron2-object-detection-manipulating","repo_url":"https://github.com/avs-abhishek123/Detectron2","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"caffe2","reach":null},{"paper_slug":"detectron2-object-detection-manipulating","repo_url":"https://github.com/facebookresearch/detectron2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"data-visualization","task_name":"Data Visualization"},{"task_slug":"image-augmentation","task_name":"Image Augmentation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-manipulation","task_name":"Image Manipulation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"panoptic-segmentation","task_name":"Panoptic Segmentation"},{"task_slug":"real-time-object-detection","task_name":"Real-Time Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bilateral-grid","method_name":"Bilateral Grid"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dcnn","method_name":"DCNN"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"image-scale-augmentation","method_name":"Image Scale Augmentation"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"mask-r-cnn","method_name":"Mask R-CNN"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}