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, a Squeezed Edge YOLO is proposed which is compressed and optimized to kilobytes of parameters in order to fit onboard such edge devices. To evaluate the proposed Squeezed Edge YOLO, two use cases - human and shape detection - are used to show the model accuracy and performance. Moreover, the proposed model is deployed onboard a GAP8 processor with 8 RISC-V cores and an NVIDIA Jetson Nano with 4GB of memory. Experimental results shows the proposed 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.