Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based Techniques
Recent Deep Neural Networks (DNNs) based edge detection methods claim to beat human performance on small scale datasets like BSDS500. But is the problem of edge detection really solved? To answer this question, we review the existing dataset limitations as well as the generalization capabilities of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9812621/ |
| _version_ | 1828427215836020736 |
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| author | Muhammad Mubashar Naeemullah Khan Abdur Rehman Sajid Muhammad Hashim Javed Naveed Ul Hassan |
| author_facet | Muhammad Mubashar Naeemullah Khan Abdur Rehman Sajid Muhammad Hashim Javed Naveed Ul Hassan |
| author_sort | Muhammad Mubashar |
| collection | DOAJ |
| description | Recent Deep Neural Networks (DNNs) based edge detection methods claim to beat human performance on small scale datasets like BSDS500. But is the problem of edge detection really solved? To answer this question, we review the existing dataset limitations as well as the generalization capabilities of the proposed architectures. To this end, we develop a Synthetic Textured Masks Dataset (STMD) that contains 28,000 grayscale images. The performance of several edge detection methods is severely degraded on STMD. To further validate these results we propose a baseline Single Scale Feed Forward Edge Detector (SFED), which is a simple 9-layer feed-forward convolutional neural network with no pooling layers. The performance of SFED is better than most state-of-the-art architectures on BSDS500 and is superior to all the compared architectures on STMD. These results show that most of the architectural advancements of existing architectures are at the cost of generalizability where if we change the dataset set distribution (both training and testset), the performance becomes significantly degraded and therefore the problem of edge detection is still far away from being solved. There are also severe limitations of existing datasets in the field, and STMD provides a framework for designing and testing better edge detection architectures for novel application areas, such as, medical imaging and self-driving cars. |
| first_indexed | 2024-12-10T16:58:07Z |
| format | Article |
| id | doaj.art-95da5eab44b14db1836780ecdd3a8dcc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T16:58:07Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-95da5eab44b14db1836780ecdd3a8dcc2022-12-22T01:40:40ZengIEEEIEEE Access2169-35362022-01-0110705417055210.1109/ACCESS.2022.31878389812621Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based TechniquesMuhammad Mubashar0Naeemullah Khan1Abdur Rehman Sajid2Muhammad Hashim Javed3https://orcid.org/0000-0001-5182-3427Naveed Ul Hassan4https://orcid.org/0000-0001-5477-6195Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore, PakistanDepartment of Engineering Sciences, University of Oxford, Oxford, U.KDepartment of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore, PakistanDepartment of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore, PakistanDepartment of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore, PakistanRecent Deep Neural Networks (DNNs) based edge detection methods claim to beat human performance on small scale datasets like BSDS500. But is the problem of edge detection really solved? To answer this question, we review the existing dataset limitations as well as the generalization capabilities of the proposed architectures. To this end, we develop a Synthetic Textured Masks Dataset (STMD) that contains 28,000 grayscale images. The performance of several edge detection methods is severely degraded on STMD. To further validate these results we propose a baseline Single Scale Feed Forward Edge Detector (SFED), which is a simple 9-layer feed-forward convolutional neural network with no pooling layers. The performance of SFED is better than most state-of-the-art architectures on BSDS500 and is superior to all the compared architectures on STMD. These results show that most of the architectural advancements of existing architectures are at the cost of generalizability where if we change the dataset set distribution (both training and testset), the performance becomes significantly degraded and therefore the problem of edge detection is still far away from being solved. There are also severe limitations of existing datasets in the field, and STMD provides a framework for designing and testing better edge detection architectures for novel application areas, such as, medical imaging and self-driving cars.https://ieeexplore.ieee.org/document/9812621/Computer visionedge detectionsegmentation |
| spellingShingle | Muhammad Mubashar Naeemullah Khan Abdur Rehman Sajid Muhammad Hashim Javed Naveed Ul Hassan Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based Techniques IEEE Access Computer vision edge detection segmentation |
| title | Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based Techniques |
| title_full | Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based Techniques |
| title_fullStr | Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based Techniques |
| title_full_unstemmed | Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based Techniques |
| title_short | Have We Solved Edge Detection? A Review of State-of-the-Art Datasets and DNN Based Techniques |
| title_sort | have we solved edge detection a review of state of the art datasets and dnn based techniques |
| topic | Computer vision edge detection segmentation |
| url | https://ieeexplore.ieee.org/document/9812621/ |
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