Authors:
Shi Guo
;
Yang Liu
;
Yong Ni
and
Wei Ni
Affiliation:
Jiangsu Automation Research Institute, Lianyungang, China
Keyword(s):
Object Detection, CNN Detectors, Lightweight SSD Object Detector, Circle Feature Pyramid Networks, MBlitenet, Bag of Freebies.
Abstract:
Computer vision has a wide range of applications, and the current demand for intelligent embedded terminals is increasing. However, most research on CNN (Convolutional Neural Network) detectors did not consider mobile devices' limited computation and did not specifically design networks for mobile devices. To achieve an efficient object detector for mobile devices, we propose a lightweight detector named Lightweight SSD. In the backbone part, we design our MBlitenet backbone based on the Attentive linear inverted residual bottleneck to enhance the backbone's feature extraction capability while achieving the lightweight requirements. In the detection neck part, we propose an efficient feature fusion network CFPN. Two innovative and useful Bag of freebies named BLL loss (Both Localization Loss) and GrayMixRGB are applied to the Lightweight SSD’s training procedure. They can further improve detector capabilities and efficiency without increasing the inference computation. As a result, L
ightweight SSD achieves 74.4 mAP (mean Average Precision) with only 4.86M parameters on PASCAL VOC, being 0.2x smaller yet still more accurate 3.5 mAP than the previous best lightweight detector. To our knowledge, the Lightweight SSD is the state-of-the-art real-time lightweight detector on mobile devices with the edge Application-specific integrated circuit (ASIC). Source Code will be released after paper publication.
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