Learning Feature Fusion in Deep Learning-Based Object Detector
Object detection in real images is a challenging problem in computer vision. Despite several advancements in detection and recognition techniques, robust and accurate localization of interesting objects in images from real-life scenarios remains unsolved because of the difficulties posed by intracla...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/7286187 |
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| author | Ehtesham Hassan Yasser Khalil Imtiaz Ahmad |
| author_facet | Ehtesham Hassan Yasser Khalil Imtiaz Ahmad |
| author_sort | Ehtesham Hassan |
| collection | DOAJ |
| description | Object detection in real images is a challenging problem in computer vision. Despite several advancements in detection and recognition techniques, robust and accurate localization of interesting objects in images from real-life scenarios remains unsolved because of the difficulties posed by intraclass and interclass variations, occlusion, lightning, and scale changes at different levels. In this work, we present an object detection framework by learning-based fusion of handcrafted features with deep features. Deep features characterize different regions of interest in a testing image with a rich set of statistical features. Our hypothesis is to reinforce these features with handcrafted features by learning the optimal fusion during network training. Our detection framework is based on the recent version of YOLO object detection architecture. Experimental evaluation on PASCAL-VOC and MS-COCO datasets achieved the detection rate increase of 11.4% and 1.9% on the mAP scale in comparison with the YOLO version-3 detector (Redmon and Farhadi 2018). An important step in the proposed learning-based feature fusion strategy is to correctly identify the layer feeding in new features. The present work shows a qualitative approach to identify the best layer for fusion and design steps for feeding in the additional feature sets in convolutional network-based detectors. |
| format | Article |
| id | doaj-art-00c00a48371f41b9ac830fc8d889db3c |
| institution | DOAJ |
| issn | 2314-4904 2314-4912 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-00c00a48371f41b9ac830fc8d889db3c2025-08-20T03:22:31ZengWileyJournal of Engineering2314-49042314-49122020-01-01202010.1155/2020/72861877286187Learning Feature Fusion in Deep Learning-Based Object DetectorEhtesham Hassan0Yasser Khalil1Imtiaz Ahmad2Department of Computer Science and Engineering, Kuwait College of Science and Technology, Kuwait City, KuwaitUniversity of Ottawa, Ottawa, CanadaDepartment of Computer Engineering, Kuwait University, Kuwait City, KuwaitObject detection in real images is a challenging problem in computer vision. Despite several advancements in detection and recognition techniques, robust and accurate localization of interesting objects in images from real-life scenarios remains unsolved because of the difficulties posed by intraclass and interclass variations, occlusion, lightning, and scale changes at different levels. In this work, we present an object detection framework by learning-based fusion of handcrafted features with deep features. Deep features characterize different regions of interest in a testing image with a rich set of statistical features. Our hypothesis is to reinforce these features with handcrafted features by learning the optimal fusion during network training. Our detection framework is based on the recent version of YOLO object detection architecture. Experimental evaluation on PASCAL-VOC and MS-COCO datasets achieved the detection rate increase of 11.4% and 1.9% on the mAP scale in comparison with the YOLO version-3 detector (Redmon and Farhadi 2018). An important step in the proposed learning-based feature fusion strategy is to correctly identify the layer feeding in new features. The present work shows a qualitative approach to identify the best layer for fusion and design steps for feeding in the additional feature sets in convolutional network-based detectors.http://dx.doi.org/10.1155/2020/7286187 |
| spellingShingle | Ehtesham Hassan Yasser Khalil Imtiaz Ahmad Learning Feature Fusion in Deep Learning-Based Object Detector Journal of Engineering |
| title | Learning Feature Fusion in Deep Learning-Based Object Detector |
| title_full | Learning Feature Fusion in Deep Learning-Based Object Detector |
| title_fullStr | Learning Feature Fusion in Deep Learning-Based Object Detector |
| title_full_unstemmed | Learning Feature Fusion in Deep Learning-Based Object Detector |
| title_short | Learning Feature Fusion in Deep Learning-Based Object Detector |
| title_sort | learning feature fusion in deep learning based object detector |
| url | http://dx.doi.org/10.1155/2020/7286187 |
| work_keys_str_mv | AT ehteshamhassan learningfeaturefusionindeeplearningbasedobjectdetector AT yasserkhalil learningfeaturefusionindeeplearningbasedobjectdetector AT imtiazahmad learningfeaturefusionindeeplearningbasedobjectdetector |