{"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/fusing-event-based-and-rgb-camera-for-robust","title":"Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions","arxiv_id":null,"date":"2022-03-30","proceeding":"ICRA 2022 3","authors":["Abhishek Tomy","Anshul Paigwar","Khushdeep Singh Mann","Alessandro Renzaglia","Christian Laugier"],"abstract":"The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving. Traditional RGB-based detection fails under these conditions and it is thus important to design a sensor suite that is redundant to failures of the primary frame-based detection. Event-based cameras can complement frame-based cameras in low-light conditions and high dynamic range scenarios that an autonomous vehicle can encounter during navigation. Accordingly, we propose a redundant sensor fusion model of event-based and frame-based cameras that is robust to common image corruptions. The method utilizes a voxel grid representation for events as input and proposes a two-parallel feature extractor network for frames and events. Our sensor fusion approach is more robust to corruptions by over 30% compared to only frame-based detections and outperforms the only event-based detection. The model is trained and evaluated on the publicly released DSEC dataset.","url_abs":"https://hal.archives-ouvertes.fr/hal-03591717/","url_pdf":"https://hal.archives-ouvertes.fr/hal-03591717/document","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":"fusing-event-based-and-rgb-camera-for-robust","repo_url":"https://github.com/abhishek1411/event-rgb-fusion","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"adversarial-attack","task_name":"Adversarial Attack"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"event-based-vision","task_name":"Event-based vision"},{"task_slug":"infrared-and-visible-image-fusion","task_name":"Infrared And Visible Image Fusion"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"robust-object-detection","task_name":"Robust Object Detection"},{"task_slug":"sensor-fusion","task_name":"Sensor Fusion"},{"task_slug":"stereo-lidar-fusion","task_name":"Stereo-LiDAR Fusion"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-dsec","task":"Object Detection","dataset":"DSEC","model":"FPN-Fusion","rank_in_archive_order":10,"of":12,"metrics":{"mAP":"24.4"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-eventped","task":"Object Detection","dataset":"EventPed","model":"FPN-Fusion","rank_in_archive_order":2,"of":6,"metrics":{"AP":"61.1"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-inoutdoor","task":"Object Detection","dataset":"InOutDoor","model":"FPN-Fusion","rank_in_archive_order":4,"of":6,"metrics":{"AP":"60.1"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-pku-ddd17-car","task":"Object Detection","dataset":"PKU-DDD17-Car","model":"FPN-Fusion","rank_in_archive_order":7,"of":14,"metrics":{"mAP50":"81.9"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-stcrowd","task":"Object Detection","dataset":"STCrowd","model":"FPN-Fusion","rank_in_archive_order":2,"of":6,"metrics":{"AP":"61.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}