A Small Target Pedestrian Detection Model Based on Autonomous Driving
Since small-target pedestrians account for a small proportion of pixels in images and lack texture features, the feature information of small-target pedestrians is often ignored in the feature extraction process, leading to reduced accuracy and poor robustness. To improve the accuracy of small-targe...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/5349965 |
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| _version_ | 1849467830715547648 |
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| author | Yang Zhang Shuaifeng Zhang Dongrong Xin Dewang Chen |
| author_facet | Yang Zhang Shuaifeng Zhang Dongrong Xin Dewang Chen |
| author_sort | Yang Zhang |
| collection | DOAJ |
| description | Since small-target pedestrians account for a small proportion of pixels in images and lack texture features, the feature information of small-target pedestrians is often ignored in the feature extraction process, leading to reduced accuracy and poor robustness. To improve the accuracy of small-target pedestrian detection and the anti-interference ability of the model, a small-target pedestrian detection model that fuses residual networks and feature pyramids is proposed. First, a residual block with a discard layer is constructed to replace the standard residual block in the residual network structure to reduce the complexity of the model computation process and solve the problems of gradient disappearance and explosion in the deep network. Then, feature selection and feature alignment modules are added to the lateral connection part of the feature pyramid to enhance important pedestrian features in the input image, and the multiscale feature fusion capability of the model is enhanced for small-target pedestrians, thereby improving the detection accuracy of small-target pedestrians and solving the problems of feature misalignment and ignored multiscale features in the feature pyramid network. Finally, a cascaded autofocus query module is proposed to increase the inference speed of the feature pyramid network through focusing and querying, thus improving the performance and efficiency of small-target pedestrian detection. The experimental results show that the proposed model achieves better detection results than previous models. |
| format | Article |
| id | doaj-art-e6aec3ef2dd44207bbae93f0cb36e6e5 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e6aec3ef2dd44207bbae93f0cb36e6e52025-08-20T03:26:03ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/5349965A Small Target Pedestrian Detection Model Based on Autonomous DrivingYang Zhang0Shuaifeng Zhang1Dongrong Xin2Dewang Chen3School of TransportationSchool of TransportationSchool of TransportationSchool of TransportationSince small-target pedestrians account for a small proportion of pixels in images and lack texture features, the feature information of small-target pedestrians is often ignored in the feature extraction process, leading to reduced accuracy and poor robustness. To improve the accuracy of small-target pedestrian detection and the anti-interference ability of the model, a small-target pedestrian detection model that fuses residual networks and feature pyramids is proposed. First, a residual block with a discard layer is constructed to replace the standard residual block in the residual network structure to reduce the complexity of the model computation process and solve the problems of gradient disappearance and explosion in the deep network. Then, feature selection and feature alignment modules are added to the lateral connection part of the feature pyramid to enhance important pedestrian features in the input image, and the multiscale feature fusion capability of the model is enhanced for small-target pedestrians, thereby improving the detection accuracy of small-target pedestrians and solving the problems of feature misalignment and ignored multiscale features in the feature pyramid network. Finally, a cascaded autofocus query module is proposed to increase the inference speed of the feature pyramid network through focusing and querying, thus improving the performance and efficiency of small-target pedestrian detection. The experimental results show that the proposed model achieves better detection results than previous models.http://dx.doi.org/10.1155/2023/5349965 |
| spellingShingle | Yang Zhang Shuaifeng Zhang Dongrong Xin Dewang Chen A Small Target Pedestrian Detection Model Based on Autonomous Driving Journal of Advanced Transportation |
| title | A Small Target Pedestrian Detection Model Based on Autonomous Driving |
| title_full | A Small Target Pedestrian Detection Model Based on Autonomous Driving |
| title_fullStr | A Small Target Pedestrian Detection Model Based on Autonomous Driving |
| title_full_unstemmed | A Small Target Pedestrian Detection Model Based on Autonomous Driving |
| title_short | A Small Target Pedestrian Detection Model Based on Autonomous Driving |
| title_sort | small target pedestrian detection model based on autonomous driving |
| url | http://dx.doi.org/10.1155/2023/5349965 |
| work_keys_str_mv | AT yangzhang asmalltargetpedestriandetectionmodelbasedonautonomousdriving AT shuaifengzhang asmalltargetpedestriandetectionmodelbasedonautonomousdriving AT dongrongxin asmalltargetpedestriandetectionmodelbasedonautonomousdriving AT dewangchen asmalltargetpedestriandetectionmodelbasedonautonomousdriving AT yangzhang smalltargetpedestriandetectionmodelbasedonautonomousdriving AT shuaifengzhang smalltargetpedestriandetectionmodelbasedonautonomousdriving AT dongrongxin smalltargetpedestriandetectionmodelbasedonautonomousdriving AT dewangchen smalltargetpedestriandetectionmodelbasedonautonomousdriving |