A critical mission for mobile robot vision is to detect and classify different objects with low power consumption. In this brief, a multi-class object detection coprocessor is proposed by using the Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) together with a weighted Softmax classifier. Its architecture is compact and hardware-friendly suitable for energy-constrained applications. Firstly, the cell-based feature extraction unit and block-level normalization reuse the SRAMs for storing one-row cell and one-row block. Meanwhile, the working frequency of the feature extraction and block-normalization unit is synchronized to the image sensor for low dynamic power. Then, a block-level one-time sliding-detection-window (OTSDW) mechanism is developed for partial classification with scalable object size. Finally, the Softmax classifier, which is implemented by the look-up table, linear fitting methods, and the fixed-point number, is tested in the Fashion MNIST dataset to evaluate its performance in multi-class classification problems and it reached an accuracy of over 86.2% with 10,180 parameters. The experimental result shows that the hardware-resource usage of the FPGA implementation is capable of 60 fps VGA video with 80.98 mW power consumption. This method uses similar or even fewer hardware resources than that of previous work using only the HOG feature and single-class classifier.

中文翻译：

---

具有双特征空间和加权 Softmax 的多类对象检测协处理器

移动机器人视觉的一个关键任务是以低功耗检测和分类不同的物体。在这个简介中，通过使用定向梯度直方图 (HOG) 和局部二进制模式 (LBP) 以及加权 Softmax 分类器，提出了一种多类对象检测协处理器。其架构紧凑且硬件友好，适用于能源受限的应用。首先，基于单元的特征提取单元和块级归一化重用SRAM来存储一排单元和一排块。同时，特征提取和块归一化单元的工作频率与图像传感器同步，动态功耗低。然后，开发了一种块级一次性滑动检测窗口（OTSDW）机制，用于具有可扩展对象大小的部分分类。最后，由查找表、线性拟合方法和定点数实现的Softmax分类器在Fashion MNIST数据集中进行测试以评估其在多类分类问题中的性能，其准确率达到86.2以上% 有 10,180 个参数。实验结果表明，FPGA 实现的硬件资源使用能够以 80.98 mW 的功耗处理 60 fps VGA 视频。与之前仅使用 HOG 特征和单类分类器的工作相比，此方法使用的硬件资源相似甚至更少。实验结果表明，FPGA 实现的硬件资源使用能够以 80.98 mW 的功耗处理 60 fps VGA 视频。与之前仅使用 HOG 特征和单类分类器的工作相比，此方法使用的硬件资源相似甚至更少。实验结果表明，FPGA 实现的硬件资源使用能够以 80.98 mW 的功耗处理 60 fps VGA 视频。与之前仅使用 HOG 特征和单类分类器的工作相比，此方法使用的硬件资源相似甚至更少。