基于SSD算法的轻量化仪器表盘检测算法

在使用传统的图像识别算法对仪器表盘中的数字进行识别时，存在着流程繁琐，处理时间较长和检测效果不佳等问题。针对上述不足，提出了一种基于深度学习的轻量化仪器表盘检测算法，该算法以单发多尺度检测算法为基础，使用深度可分离卷积代替标准卷积设计特征提取网络，以提升特征表达能力和轻量化性能；同时提出了一种基于真实框分布构建锚框的流程，设计了能量化表达锚框匹配程度的指标——匹配率，促进构建匹配度更高且锚框数量更少的锚框方案。实验结果表明，所提算法具有较少的模型参数量和计算量，具有较高的检测精度，并且可在CPU环境下满足实时检测需求。

A lightweight instrument dial detectionalgorithm based on SSD algorithm

There are problems such as cumbersome process, long processing time, and poor detection effect when the traditional image recognition algorithm is used to recognize the numbers in the instrument dial. Aiming at the above problems, a lightweight instrument dial detection method based on deep learning is proposed. Based on a single-shot multi-scale detection method (SSD), the proposal uses deep separable convolution instead of standard convolution to design feature extraction networks to improve feature expression capabilities and lightweight performance. At the same time, an anchor boxes construction process based on the distribution of ground truth boxes is proposed. In the process, an index that can quantify the matching degree of anchor frames (matching rate) is designed, and an anchor box scheme with a higher matching rate and a smaller number is constructed. Experimental results show that the proposed algorithm has less model parameters and computation, higher detection accuracy, and can obtain real-time processing speed in CPU environment.