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Specialized Indoor and Outdoor Scene-specific Object Detection Models
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-9464-7010
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-0998-6585
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-3797-4605
Axis Communications AB, Lund, Sweden.
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2023 (English)In: Sixteenth International Conference on Machine Vision (ICMV 2023) / [ed] Osten, Wolfgang, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Object detection is a critical task in computer vision with applications across various domains, ranging from autonomous driving to surveillance systems. Despite extensive research on improving the performance of object detection systems, identifying all objects in different places remains a challenge. The traditional object detection approaches focus primarily on extracting and analyzing visual features without considering the contextual information about the places of objects. However, entities in many real-world scenarios closely relate to their surrounding environment, providing crucial contextual cues for accurate detection. This study investigates the importance and impact of places of images (indoor and outdoor) on object detection accuracy. To this purpose, we propose an approach that first categorizes images into two distinct categories: indoor and outdoor. We then train and evaluate three object detection models (indoor, outdoor, and general models) based on YOLOv5 and 19 classes of the PASCAL VOC dataset and 79 classes of COCO dataset that consider places. The experimental evaluations show that the specialized indoor and outdoor models have higher mAP (mean Average Precision) to detect objects in specific environments compared to the general model that detects objects found both indoors and outdoors. Indeed, the network can detect objects more accurately in similar places with common characteristics due to semantic relationships between objects and their surroundings, and the network’s misdetection is diminished. All the results were analyzed statistically with t-tests.

Place, publisher, year, edition, pages
2023.
Series
Proceedings of SPIE, ISSN 0277-786X, E-ISSN 1996-756X ; 13072
Keywords [en]
object detection, YOLOv5, indoor object detection, outdoor object detection, scene classification
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mau:diva-66441DOI: 10.1117/12.3023479ISI: 001208308300024Scopus ID: 2-s2.0-85191658757ISBN: 9781510674622 (print)ISBN: 9781510674639 (electronic)OAI: oai:DiVA.org:mau-66441DiVA, id: diva2:1846539
Conference
International Conference on Machine Vision (ICMV 2023), Nov. 15-18, 2023, Yerevan, Armenia
Available from: 2024-03-22 Created: 2024-03-22 Last updated: 2024-05-20Bibliographically approved

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Jamali, MahtabDavidsson, PaulKhoshkangini, RezaMihailescu, Radu-Casian

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