FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving

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.

FRDet：用于嵌入式自动驾驶处理器的基于火灾残差模块的平衡轻型物体检测器

为了部署在用于自动驾驶的嵌入式处理器上，对象检测网络应满足所有的精度，实时推理和灯光模型尺寸要求。常规的基于CNN的深度检测器追求高精度，从而使其模型尺寸对于内存空间有限的嵌入式系统而言非常沉重。.. 相反，轻量物体检测器被大大压缩，但是却大大牺牲了精度。因此，我们提出了FRDet，这是一种轻量级的一级目标检测器，经过平衡后可以满足在自动驾驶应用中嵌入式GPU处理器上的精度，模型大小和实时处理的所有限制。我们的网络旨在在达到或超过YOLOv3准确性水平的同时，最大程度地压缩模型。本文提出了“残火”（FR）模块，通过使用残差跳过连接适配火模块来设计一种具有低精度损失的轻型网络。另外，采用边界框的高斯不确定性建模来进一步提高定位精度。在KITTI数据集上进行的实验表明，FRDet将内存大小减少了50.8％，但将精度提高了1。相较于YOLOv3，mAP为12％。此外，在嵌入式GPU板上（NVIDIA Xavier）的实时检测速度达到31.3 FPS。与其他深层CNN物体探测器相比，拟议的网络以更高的精度实现了更高的压缩率，同时显示出比轻型探测器基线更高的精度。因此，提出的FRDet是一种在自动驾驶中实际应用的均衡且高效的对象检测器，它可以满足准确性，实时推断和光照模型大小的所有标准。 （阅读更多）