@inproceedings {New Orleans, Louisiana,
	title = {Light-Weight RetinaNet for Object Detection on Edge Devices},
	year = {2020},
	month = {04/2020},
	address = {New Orleans, Louisiana},
	abstract = {<p>This paper aims at reducing computation for Reti\&shy;nanet, an mAP-30-tier network, to facilitate its practical deploy\&shy;ment on edge devices for providing IoT-based object detection services. We first validate RetinaNet has the best FLOP-mAP trade-off among all mAP-30-tier network. Then, we propose a light-weight RetinaNet structure with effective computation\&shy;-accuracy trade-off by only reducing FLOPs in computationally intensive layers. Compared with the most common way of trading off computation with accuracy-input image scaling, the proposed solution shows a consistently better FLOPs-mAP trade-off curve. Light-weight RetinaNet achieves a 0.3\% mAP improvement at 1.Sx FLOPs reduction point over the original RetinaNet, and gains 1.Sx more energy-efficiency on an Intel Arria 10 FPGA accelerator in the context of edge computing. The proposed method potentially can help a wide range of the object detection applications to move closer to a preferred corner for a better runtime and accuracy, while enjoys more energy-efficient inference at the edge.\&nbsp;</p>
},
	keywords = {psclab},
	author = {YIxing Li and Akshay Dua and Fengbo Ren}
}
