[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123359-en":3,"doc-seo-123359-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123359,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Classification of Road Scenes Based on Heterogeneous Features and Machine Learning - Original Scientific Paper","Rapid advances in AI and machine learning have strengthened object detection for smart vehicles, yet autonomous systems still face difficulty achieving higher accuracy with faster response under safety constraints. This article presents a heterogeneous-features machine learning framework for road scene distinction using four datasets. Object-based features use YOLOv5m and modified VGG19, image-based features use modified VGG19, and conventional descriptors (Matched filters, Wavelets, GLCM, LBP, HOG) enhance fine and coarse cues. Experiments report 97.62% average accuracy and up to 99.85% between crosswalks and Parking.","Classification of Road Scenes Based on  \nHeterogeneous Features and Machine Learning  \nOriginal Scientific Paper  \nSanjay P. Pande  \nYeshwantrao Chavan College of Engineering, Department of Computer Technology, Hingna, Nagpur, Maharashtra, India [sanjaypande2001@gmail.com](sanjaypande2001@gmail.com)  \nSarika Khandelwal  \nG H Raisoni College of Engineering,  \nDepartment of Computer Science and Engineering Digdoh Hills, Nagpur, Maharashtra, India [sarikakhandelwal@gmail.com](sarikakhandelwal@gmail.com)  \n[Pratik R](Pratik R). Hajare  \nMansarovar Global University,  \nDepartment of Electrical and Electronics Engineering Raison Road, Bhopal, Madhya Pradesh, India [pratikhajare8@gmail.com](pratikhajare8@gmail.com)  \n*Corresponding author  \nPoonamT. Agarkar*  \nRamdeobaba University,  \nSchool of Computer Science and Engineering Katol Raod, Nagpur, Maharashtra, India [agarkarp@rknec.edu](agarkarp@rknec.edu)  \n[Rajani D. Singh](Rajani D. Singh)  \nBallarpur Institute of Technology, Department of Master of Computer Application Ballarpur, Chandrapur, Maharashtra, India [rajanidsingh@gmail.com](rajanidsingh@gmail.com)  \nPrashant R. Patil  \nSmt. Radhikatai Pandav College of Engineering, Department of Management Studies  \nUmrer Road, Nagpur, Maharashtra, India [patilnagpur@gmail.com](patilnagpur@gmail.com)  \nAbstract – There is a rapid advancement in Artificial intelligence (AI) and Machine Learning (ML) that has extensively improved the object detection capabilities of smart vehicles today. Convolutional Neural Networks (CNNs) based on small, medium, and large networks have made significant contributions to in-vehicle navigation. Simultaneously, achieving higher level accuracies and faster response in autonomous vehicles is still a challenge and needs special care and attention and must be addressed for human safety. Hence, this article proposes a heterogeneous features-based machine learning framework to distinguish road scenes. The model incorporates object-based, image-based, and diverse conventional features from the road scene images generated from four distinct datasets. Object-based features are acquired using the YOLOv5m model and modified VGG19 networks, whereas image-based features are extracted using the modified VGG19 network. Conventional features are added to the object-based and blind features by applying a variety of descriptors that include Matched filters, Wavelets, Gray Level Occurrence Matrix (GLCM), Linear Binary Pattern (LBP), and Histogram of Gaussian (HOG). The descriptors are used to extract fine and course features to enhance the capabilities of the classifier. Experiments show that the proposed road scene classification framework performed better in classifying two scene categories, including crosswalks, parking, roads under bridges/tunnels, and highways achieving an average classification accuracy of 97.62% and the highest of 99.85% between crosswalks and Parking. A marginal improvement of approximately 1% is seen when all four categories were considered for evaluation using a multiclass SVM compared to other competing models.  \nKeywords: Artificial intelligence, Machine Learning, smart vehicles, CNN, object-based, image-based, diverse conventional features, YOLOv5m, and VGG19.  \nReceived: July 27, 2024; Received in revised form: December 16, 2024; Accepted: December 23, 2024  \n1. INTRODUCTION  \nSafer autonomous vehicles work on algorithms based on computer vision that can distinguish certain scenarios and accurately predict labels. Related scenes consist of several details and are infinite. Varying image classification achievements are significant and include a wide range of image classes [1, 2] . Remarkable results  \nhave been obtained on the ImageNet dataset using convolutional neural networks and frequent improvements are suggested by many researchers [3] . However, further initiatives are needed in scene categorization to improve visual perception in autonomous driving. Work introduced in [4] considered 2.5 m","cbCairVXMwre3hxz","https://ap.wps.com/l/cbCairVXMwre3hxz","pdf",3261532,1,12,"English","en",105,"# INTRODUCTION\n## Related work and motivation\n## Proposed approach overview","[{\"question\":\"Why is road scene classification important for autonomous vehicles?\",\"answer\":\"Safer autonomous vehicles rely on computer-vision algorithms to distinguish scenarios and predict labels accurately. Improving scene categorization directly supports better visual perception for driving safety.\"},{\"question\":\"What heterogeneous features does the proposed framework use?\",\"answer\":\"It combines object-based features (YOLOv5m and modified VGG19), image-based features (modified VGG19), and conventional descriptor-based features such as Matched filters, Wavelets, GLCM, LBP, and HOG.\"},{\"question\":\"How well does the proposed method perform in experiments?\",\"answer\":\"It achieves an average classification accuracy of 97.62% for two scene categories and a highest accuracy of 99.85% between crosswalks and Parking, with about a 1% marginal improvement when evaluating four categories using a multiclass SVM.\"}]","Classification of Road Scenes Based on Heterogeneous Features and Machine Learning - Original Scientific Paper | PDF",1785816114,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"classification-of-road-scenes-based-on-heterogeneous-features-and-machine-learning-original-scientific-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/classification-of-road-scenes-based-on-heterogeneous-features-and-machine-learning-original-scientific-paper/123359/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is road scene classification important for autonomous vehicles?","Question",{"text":75,"@type":76},"Safer autonomous vehicles rely on computer-vision algorithms to distinguish scenarios and predict labels accurately. Improving scene categorization directly supports better visual perception for driving safety.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What heterogeneous features does the proposed framework use?",{"text":80,"@type":76},"It combines object-based features (YOLOv5m and modified VGG19), image-based features (modified VGG19), and conventional descriptor-based features such as Matched filters, Wavelets, GLCM, LBP, and HOG.",{"name":82,"@type":73,"acceptedAnswer":83},"How well does the proposed method perform in experiments?",{"text":84,"@type":76},"It achieves an average classification accuracy of 97.62% for two scene categories and a highest accuracy of 99.85% between crosswalks and Parking, with about a 1% marginal improvement when evaluating four categories using a multiclass SVM.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]