# Fast Target Recognition Based on Improved ORB Feature
###### Abstract  
A novel fast target recognition algorithm is proposed under the dynamic scene moving target recognition. Aiming at the poor matching effect of the traditional Oriented Fast and Rotated Brief (ORB) algorithm on underexposed or overexposed images caused by illumination, the idea of combining adaptive histogram equalization with the ORB algorithm is proposed to get better feature point quality and matching efficiency. First, the template image and each frame of the video stream are processed by grayscale. Second, the template image and the image to be input in the video stream are processed by adaptive histogram equalization. Third, the feature point descriptors of the ORB feature are quantized by the Hamming distance. Finally, the K-nearest-neighbor (KNN) matching algorithm is used to match and screen feature points. According to the matching good feature point logarithm, a reasonable threshold is established and the target is classified. The comparison and verification are carried out by experiments. Experimental results show that the algorithm not only maintains the superiority of ORB itself but also significantly improves the performance of ORB under the conditions of underexposure or overexposure. The matching effect of the image is robust to illumination, and the target to be detected can be accurately identified in real time. The target can be accurately classified in the small sample scene, which can meet the actual production requirements.  
image matching; image recognition; image enhancement; ORB algorithm; adaptive histogram equalization; KNN matching +
Footnote †: journal: Medical Image Computing  
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Footnote 1: email: {[[https://www.mdpi.com/journal/applsci/data](https://www.mdpi.com/journal/applsci/data)]([https://www.mdpi.com/journal/applsci/data](https://www.mdpi.com/journal/applsci/data))  
algorithm appropriately reduces the accuracy and robustness of feature points, improves the calculation speed, and reduces the calculation time, which is a good compromise between the quality and performance of different types of feature points [9].  
Due to the good performance of the ORB characteristics [10; 11; 12; 13; 14; 15], many scholars at home and abroad have made different improvements to the ORB algorithm [16; 17; 18; 19]. [PERSON] et al. [20] matched the ORB feature point matching algorithm and eight parameters and combined the rotation model, improving the detection speed of the feature point; and [PERSON] et al. [21] improved the rotation of the ORB algorithm in the feature point matching algorithm of the ORB, which enhanced the matching accuracy; but for special scenes, the study of the ORB algorithm is rare under the conditions of poor illumination conditions. For the above questions, in this paper, the template image in the video stream and the image to be input are first processed for grayscale. The second step is to perform adaptive histogram equalization processing on the image to improve the image quality of the input image and the template image. The third step is to extract the feature description through the ORB algorithm. Finally, the KNN matching algorithm is used. By comparing the distance between the optimal feature matching and the suboptimal feature matching, a reasonable ratio is set to eliminate the mismatch. Compared with the traditional ORB matching algorithm, the improved ORB matching algorithm can significantly improve the matching performance under poor lighting conditions. Finally, by comparing the logarithm of correct matching between the image features to be input and all template image features, a reasonable threshold is set, which effectively realizes the target classification of small samples.