Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNet
This study proposes an optimized footprint recognition model based on an enhanced StarNet architecture for biometric identification in the security, medical, and criminal investigation fields. Conventional image recognition algorithms exhibit limitations in processing barefoot footprint images chara...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4578 |
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| _version_ | 1849406470641156096 |
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| author | Yujie Shen Xuemei Jiang Yabin Zhao Wenxin Xie |
| author_facet | Yujie Shen Xuemei Jiang Yabin Zhao Wenxin Xie |
| author_sort | Yujie Shen |
| collection | DOAJ |
| description | This study proposes an optimized footprint recognition model based on an enhanced StarNet architecture for biometric identification in the security, medical, and criminal investigation fields. Conventional image recognition algorithms exhibit limitations in processing barefoot footprint images characterized by concentrated feature distributions and rich texture patterns. To address this, our framework integrates an improved StarNet into the backbone of YOLOv8 architecture. Leveraging the unique advantages of element-wise multiplication, the redesigned backbone efficiently maps inputs to a high-dimensional nonlinear feature space without increasing channel dimensions, achieving enhanced representational capacity with low computational latency. Subsequently, an Encoder layer facilitates feature interaction within the backbone through multi-scale feature fusion and attention mechanisms, effectively extracting rich semantic information while maintaining computational efficiency. In the feature fusion part, a feature modulation block processes multi-scale features by synergistically combining global and local information, thereby reducing redundant computations and decreasing both parameter count and computational complexity to achieve model lightweighting. Experimental evaluations on a proprietary barefoot footprint dataset demonstrate that the proposed model exhibits significant advantages in terms of parameter efficiency, recognition accuracy, and computational complexity. The number of parameters has been reduced by 0.73 million, further improving the model’s speed. Gflops has been reduced by 1.5, lowering the performance requirements for computational hardware during model deployment. Recognition accuracy has reached 99.5%, with further improvements in model precision. Future research will explore how to capture shoeprint images with complex backgrounds from shoes worn at crime scenes, aiming to further enhance the model’s recognition capabilities in more forensic scenarios. |
| format | Article |
| id | doaj-art-15c615af6d3844738e5322997ca20e68 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-15c615af6d3844738e5322997ca20e682025-08-20T03:36:22ZengMDPI AGSensors1424-82202025-07-012515457810.3390/s25154578Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNetYujie Shen0Xuemei Jiang1Yabin Zhao2Wenxin Xie3College of Investigation, People’s Public Security University of China, Beijing 100000, ChinaTrace Examination Technology Department, Institute of Forensic Science of China, Beijing 100000, ChinaCollege of Investigation, People’s Public Security University of China, Beijing 100000, ChinaSchool of Automation and Software Engineering, Shanxi University, Taiyuan 030006, ChinaThis study proposes an optimized footprint recognition model based on an enhanced StarNet architecture for biometric identification in the security, medical, and criminal investigation fields. Conventional image recognition algorithms exhibit limitations in processing barefoot footprint images characterized by concentrated feature distributions and rich texture patterns. To address this, our framework integrates an improved StarNet into the backbone of YOLOv8 architecture. Leveraging the unique advantages of element-wise multiplication, the redesigned backbone efficiently maps inputs to a high-dimensional nonlinear feature space without increasing channel dimensions, achieving enhanced representational capacity with low computational latency. Subsequently, an Encoder layer facilitates feature interaction within the backbone through multi-scale feature fusion and attention mechanisms, effectively extracting rich semantic information while maintaining computational efficiency. In the feature fusion part, a feature modulation block processes multi-scale features by synergistically combining global and local information, thereby reducing redundant computations and decreasing both parameter count and computational complexity to achieve model lightweighting. Experimental evaluations on a proprietary barefoot footprint dataset demonstrate that the proposed model exhibits significant advantages in terms of parameter efficiency, recognition accuracy, and computational complexity. The number of parameters has been reduced by 0.73 million, further improving the model’s speed. Gflops has been reduced by 1.5, lowering the performance requirements for computational hardware during model deployment. Recognition accuracy has reached 99.5%, with further improvements in model precision. Future research will explore how to capture shoeprint images with complex backgrounds from shoes worn at crime scenes, aiming to further enhance the model’s recognition capabilities in more forensic scenarios.https://www.mdpi.com/1424-8220/25/15/4578barefoot footprintcriminal investigationStarNetYOLOv8lightweightingfeature fusion |
| spellingShingle | Yujie Shen Xuemei Jiang Yabin Zhao Wenxin Xie Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNet Sensors barefoot footprint criminal investigation StarNet YOLOv8 lightweighting feature fusion |
| title | Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNet |
| title_full | Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNet |
| title_fullStr | Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNet |
| title_full_unstemmed | Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNet |
| title_short | Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNet |
| title_sort | barefoot footprint detection algorithm based on yolov8 starnet |
| topic | barefoot footprint criminal investigation StarNet YOLOv8 lightweighting feature fusion |
| url | https://www.mdpi.com/1424-8220/25/15/4578 |
| work_keys_str_mv | AT yujieshen barefootfootprintdetectionalgorithmbasedonyolov8starnet AT xuemeijiang barefootfootprintdetectionalgorithmbasedonyolov8starnet AT yabinzhao barefootfootprintdetectionalgorithmbasedonyolov8starnet AT wenxinxie barefootfootprintdetectionalgorithmbasedonyolov8starnet |