The accurate reading of pointer meter is a crucial task in complex environments such as substations, military and aerospace. The current recognition algorithm is mainly used to identify the same type and non-tilt meter, which has limited application in real environment. This paper proposes a novel end-to-end intelligent reading method of pointer meter based on deep learning, which locates the meter and extracts the pointer simultaneously without any prior information. Especially, the pointer is directly and precisely extracted using the designed semi-pointer detection method without any handcrafted features designed in advance, which avoids the accumulated error caused by preprocessing. Based on the extracted panel object, including semi-pointer, panel center and scale characters, the indicated value of the pointer is obtained by a local angle method, which can achieve better performance than the traditional angle method by referring to the neighboring scale lines of the pointer. Experimental results demonstrate that the method is faster and more effective than some common methods. It is worth noting that this study has the advantage of being able to recognize pointer meters in complex environments such as tilt, rotation, blur and illumination. It is acceptable for the actual application requirements in real environment with a recognition accuracy of 99.20% and the average reference error of 0.34%.

周登科 1) , 杨颖 1)* , 朱杰 2) , 王库 2) 1) (中国农业大学信息与电气工程学院 北京 100083) 2) (随锐科技集团股份有限公司 北京 100192)