Research Article Lightweight Object Detection Ensemble Framework for Autonomous Vehicles in Challenging Weather Conditions Rahee Walambe , 1,2 Aboli Marathe , 2 Ketan Kotecha , 1,2 and George Ghinea 3 1 Symbiosis Institute of Technology, Symbiosis International University, Pune, India 2 Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India 3 Brunel University, London, UK Correspondence should be addressed to Ketan Kotecha; drketankotecha@gmail.com Received 22 July 2021; Revised 26 August 2021; Accepted 24 September 2021; Published 7 October 2021 Academic Editor: Anastasios D. Doulamis Copyright © 2021 Rahee Walambe et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ecomputervisionsystemsdrivingautonomousvehiclesarejudgedbytheirabilitytodetectobjectsandobstaclesinthevicinity of the vehicle in diverse environments. Enhancing this ability of a self-driving car to distinguish between the elements of its environmentunderadverseconditionsisanimportantchallengeincomputervision.Forexample,poorweatherconditionslike fog and rain lead to image corruption which can cause a drastic drop in object detection (OD) performance. e primary navigationofautonomousvehiclesdependsontheeffectivenessoftheimageprocessingtechniquesappliedtothedatacollected fromvariousvisualsensors.erefore,itisessentialtodevelopthecapabilitytodetectobjectslikevehiclesandpedestriansunder challengingconditionssuchaslikeunpleasantweather.Ensemblingmultiplebaselinedeeplearningmodelsunderdifferentvoting strategiesforobjectdetectionandutilizingdataaugmentationtoboostthemodels’performanceisproposedtosolvethisproblem. e data augmentation technique is particularly useful and works with limited training data for OD applications. Furthermore, using the baseline models significantly speeds up the OD process as compared to the custom models due to transfer learning. erefore,theensemblingapproachcanbehighlyeffectiveinresource-constraineddevicesdeployedforautonomousvehiclesin uncertain weather conditions. e applied techniques demonstrated an increase in accuracy over the baseline models and were able to identify objects from the images captured in the adverse foggy and rainy weather conditions. e applied techniques demonstrated an increase in accuracy over the baseline models and reached 32.75% mean average precision (mAP) and 52.56% averageprecision(AP)indetectingcarsintheadversefogandrainweatherconditionspresentinthedataset.eeffectivenessof multiple voting strategies for bounding box predictions on the dataset is also demonstrated. ese strategies help increase the explainability of object detection in autonomous systems and improve the performance of the ensemble techniques over the baseline models. 1. Introduction e field of object detection (OD) has evolved from the conceptualization of innovative algorithms to becoming an integralpartofapplicationsintheindustry.eadoptionof object detection in countless real-life applications has been made possible due to the advancement of detection algo- rithms and the increasing computational capabilities of processors. From surveillance systems to scene under- standing and face detection, object detection is being lev- eragedtoassisthumansthroughintelligentanalyticsandby automating arduous tasks. A recent application of object detectionthatgarnersinterestisautonomousvehiclesdueto the need for fast and accurate detectors for navigation through traffic and urban environments. In recent years, the rapid advancement of self-driving carshastransformedtheirimagefromfuturisticvehiclesfar ahead of our time to a part of an imaginable reality. e diversity of features boasted by these vehicles is increasing day by day, with special emphasis on the interpretability of the car’s decisions, ethical considerations, and overall safety [1,2].Designedusingmultiplelevelsofautomation,theself- Hindawi Computational Intelligence and Neuroscience Volume 2021, Article ID 5278820, 12 pages https://doi.org/10.1155/2021/5278820