  Driven by the ever-increasing requirements of autonomous vehicles, such as
traffic monitoring and driving assistant, deep learning-based object detection
(DL-OD) has been increasingly attractive in intelligent transportation systems.
However, it is difficult for the existing DL-OD schemes to realize the
responsible, cost-saving, and energy-efficient autonomous vehicle systems due
to low their inherent defects of low timeliness and high energy consumption. In
this paper, we propose an object detection (OD) system based on edge-cloud
cooperation and reconstructive convolutional neural networks, which is called
Edge YOLO. This system can effectively avoid the excessive dependence on
computing power and uneven distribution of cloud computing resources.
Specifically, it is a lightweight OD framework realized by combining pruning
feature extraction network and compression feature fusion network to enhance
the efficiency of multi-scale prediction to the largest extent. In addition, we
developed an autonomous driving platform equipped with NVIDIA Jetson for
system-level verification. We experimentally demonstrate the reliability and
efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively.
According to COCO2017 standard datasets with a speed of 26.6 frames per second
(FPS), the results show that the number of parameters in the entire network is
only 25.67 MB, while the accuracy (mAP) is up to 47.3%.
