Improve Real-time Object Detection with Feature Enhancement

Real-time object detection is a critical task in the field of computer vision. Current methods suffers from pooling errors and classification imbalance. In this paper, we proposed a feature enhanced framework to improve the feature extraction for efficient real-time object detection. Particularly, t...

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Bibliographic Details
Published in2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) pp. 235 - 238
Main Authors Wei, Wendian, Hu, Yu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Embedded systems
Feature enhancement
Feature extraction
Interpolation
Object detection
Optimization methods
Proposals
Quantization (signal)
Real-time
Real-time systems
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Summary:Real-time object detection is a critical task in the field of computer vision. Current methods suffers from pooling errors and classification imbalance. In this paper, we proposed a feature enhanced framework to improve the feature extraction for efficient real-time object detection. Particularly, the framework is compromised with five opti-mizations on feature enhancement: bilinear interpolation up-sampling, network fully sharing, single score map, upward rounding quantization and single layer output. We performed comprehensive experiments on a publicly available dataset DACSDC, and the achieved mean average precision (mAP) is 0.6317 with a throughput of 24.67 frame per second (FPS). Our work ranked the 6th out of 24 teams in the 2018 system design contest on the 55th Design Automatic Conference (DAC).
DOI:10.1109/ITAIC.2019.8785856