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Object Detection Post Processing Accelerator Based on Co-Design of Hardware and Software

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Publicado en:Information vol. 16, no. 1 (2025), p. 63
Autor principal: Yang, Dengtian
Otros Autores: Chen, Lan, Hao, Xiaoran, Zhang, Yiheng
Publicado:
MDPI AG
Materias:
Software
Accuracy
Co-design
Deep learning
Back propagation
Hardware
Sensors
Boxes
Classification
Inference
Target detection
Algorithms
Object recognition
Machine learning
Critical components
Design optimization
Sorting algorithms
Efficiency
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Resumen:Deep learning significantly advances object detection. Post processes, a critical component of this process, select valid bounding boxes to represent the true targets during inference and assign boxes and labels to these objects during training to optimize the loss function. However, post processes constitute a substantial portion of the total processing time for a single image. This inefficiency primarily arises from the extensive Intersection over Union (IoU) calculations required between numerous redundant bounding boxes in post processing algorithms. To reduce these redundant IoU calculations, we introduce a classification prioritization strategy during both training and inference post processes. Additionally, post processes involve sorting operations that contribute to their inefficiency. To minimize unnecessary comparisons in Top-K sorting, we have improved the bitonic sorter by developing a hybrid bitonic algorithm. These improvements have effectively accelerated the post processing. Given the similarities between the training and inference post processes, we unify four typical post processing algorithms and design a hardware accelerator based on this framework. Our accelerator achieves at least 7.55 times the speed in inference post processing compared to that of recent accelerators. When compared to the RTX 2080 Ti system, our proposed accelerator offers at least 21.93 times the speed for the training post process and 19.89 times for the inference post process, thereby significantly enhancing the efficiency of loss function minimization.
ISSN:2078-2489
DOI:10.3390/info16010063
Fuente:Advanced Technologies & Aerospace Database