Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms
Whether in intelligent transportation or autonomous driving, vehicle detection is an important part. Vehicle detection still faces many problems, such as inaccurate vehicle detection positioning and low detection accuracy in complex scenes. FCOS as a representative of anchor-free detection algorithm...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9167116 |
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| _version_ | 1832552962018246656 |
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| author | Fei Yan Hui Zhang Tianyang Zhou Zhiyong Fan Jia Liu |
| author_facet | Fei Yan Hui Zhang Tianyang Zhou Zhiyong Fan Jia Liu |
| author_sort | Fei Yan |
| collection | DOAJ |
| description | Whether in intelligent transportation or autonomous driving, vehicle detection is an important part. Vehicle detection still faces many problems, such as inaccurate vehicle detection positioning and low detection accuracy in complex scenes. FCOS as a representative of anchor-free detection algorithms was once a sensation, but now it seems to be slightly insufficient. Based on this situation, we propose an improved FCOS algorithm. The improvements are as follows: (1) we introduce a deformable convolution into the backbone to solve the problem that the receptive field cannot cover the overall goal; (2) we add a bottom-up information path after the FPN of the neck module to reduce the loss of information in the propagation process; (3) we introduce the balance module according to the balance principle, which reduces inconsistent detection of the bbox head caused by the mismatch of variance of different feature maps. To enhance the comparative experiment, we have extracted some of the most recent datasets from UA-DETRAC, COCO, and Pascal VOC. The experimental results show that our method has achieved good results on its dataset. |
| format | Article |
| id | doaj-art-0d174a24bc4d4fa7b7edd8cdb7439927 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0d174a24bc4d4fa7b7edd8cdb74399272025-02-03T05:57:19ZengWileyComplexity1099-05262021-01-01202110.1155/2021/9167116Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection AlgorithmsFei Yan0Hui Zhang1Tianyang Zhou2Zhiyong Fan3Jia Liu4College of AutomationJiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET)College of Energy and Electrical EngineeringCollege of AutomationCollege of AutomationWhether in intelligent transportation or autonomous driving, vehicle detection is an important part. Vehicle detection still faces many problems, such as inaccurate vehicle detection positioning and low detection accuracy in complex scenes. FCOS as a representative of anchor-free detection algorithms was once a sensation, but now it seems to be slightly insufficient. Based on this situation, we propose an improved FCOS algorithm. The improvements are as follows: (1) we introduce a deformable convolution into the backbone to solve the problem that the receptive field cannot cover the overall goal; (2) we add a bottom-up information path after the FPN of the neck module to reduce the loss of information in the propagation process; (3) we introduce the balance module according to the balance principle, which reduces inconsistent detection of the bbox head caused by the mismatch of variance of different feature maps. To enhance the comparative experiment, we have extracted some of the most recent datasets from UA-DETRAC, COCO, and Pascal VOC. The experimental results show that our method has achieved good results on its dataset.http://dx.doi.org/10.1155/2021/9167116 |
| spellingShingle | Fei Yan Hui Zhang Tianyang Zhou Zhiyong Fan Jia Liu Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms Complexity |
| title | Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms |
| title_full | Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms |
| title_fullStr | Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms |
| title_full_unstemmed | Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms |
| title_short | Research on Multiscene Vehicle Dataset Based on Improved FCOS Detection Algorithms |
| title_sort | research on multiscene vehicle dataset based on improved fcos detection algorithms |
| url | http://dx.doi.org/10.1155/2021/9167116 |
| work_keys_str_mv | AT feiyan researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms AT huizhang researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms AT tianyangzhou researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms AT zhiyongfan researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms AT jialiu researchonmultiscenevehicledatasetbasedonimprovedfcosdetectionalgorithms |