  Demand for efficient onboard object detection is increasing due to its key
role in autonomous navigation. However, deploying object detection models such
as YOLO on resource constrained edge devices is challenging due to the high
computational requirements of such models. In this paper, an compressed object
detection model named Squeezed Edge YOLO is examined. This model is compressed
and optimized to kilobytes of parameters in order to fit onboard such edge
devices. To evaluate Squeezed Edge YOLO, two use cases - human and shape
detection - are used to show the model accuracy and performance. Moreover, the
model is deployed onboard a GAP8 processor with 8 RISC-V cores and an NVIDIA
Jetson Nano with 4GB of memory. Experimental results show Squeezed Edge YOLO
model size is optimized by a factor of 8x which leads to 76% improvements in
energy efficiency and 3.3x faster throughout.
