  For deployment on an embedded processor for autonomous driving, the object
detection network should satisfy all of the accuracy, real-time inference, and
light model size requirements. Conventional deep CNN-based detectors aim for
high accuracy, making their model size heavy for an embedded system with
limited memory space. In contrast, lightweight object detectors are greatly
compressed but at a significant sacrifice of accuracy. Therefore, we propose
FRDet, a lightweight one-stage object detector that is balanced to satisfy all
the constraints of accuracy, model size, and real-time processing on an
embedded GPU processor for autonomous driving applications. Our network aims to
maximize the compression of the model while achieving or surpassing YOLOv3
level of accuracy. This paper proposes the Fire-Residual (FR) module to design
a lightweight network with low accuracy loss by adapting fire modules with
residual skip connections. In addition, the Gaussian uncertainty modeling of
the bounding box is applied to further enhance the localization accuracy.
Experiments on the KITTI dataset showed that FRDet reduced the memory size by
50.8% but achieved higher accuracy by 1.12% mAP compared to YOLOv3. Moreover,
the real-time detection speed reached 31.3 FPS on an embedded GPU board(NVIDIA
Xavier). The proposed network achieved higher compression with comparable
accuracy compared to other deep CNN object detectors while showing improved
accuracy than the lightweight detector baselines. Therefore, the proposed FRDet
is a well-balanced and efficient object detector for practical application in
autonomous driving that can satisfies all the criteria of accuracy, real-time
inference, and light model size.
