# Detection of Logos of Moving Vehicles under Complex Lighting Conditions
## 2 Background  
The main purpose of logo recognition is to classify logo images. Image classification algorithms generally cover feature representation and classifier-based classification. The initial feature representation usually adopts basic features, such as color, texture, and shape [12], to represent image features. In the early periods, logo detection in natural scenes was dominated by logo detection in videos [13]. [PERSON] and [PERSON] [14] proposed a method to determine logo regions through strong contrast between the foreground and background in a color region and realized logo detection by using the template matching in a linear relationship with logos so as to classify logo regions. However, due to strict requirements for logo backgrounds, this algorithm cannot be used for logo detection in complex backgrounds. The traditional local feature points, such as Scale Invariant Feature Transform (SIFT) [15], Histograms of Oriented Gradients (HOG) [16], and Speeded Up Robust Features (SURF) [17], have promoted the rapid development of object recognition, pedestrian detection and face recognition in computer vision processing. For example, [PERSON] proposed a logo detection system--the novel algorithmic pipeline based on SURF combined with RANSAC-lel, which is able to detect multiple logo occurrences in the same image and maintain detection accuracy even under heavy occlusions [18]. Moreover, this algorithm can significantly improve the logo detection effect, but it is very time-consuming. The Retinex algorithm [8], which introduced the wavelet transform, also emerged as a proper balance between image color fidelity and detail enhancement. An improved algorithm based on HSV space [19] can effectively improve the image color distortion; some researchers have improved the algorithm as a whole by introducing the concept of non-neighborhood pixel constraints [19], which converts the image decomposition problem into a quadratic function minimization problem. Nevertheless, this method is time-costly, and its threshold value has to be determined artificially and is not adaptive. For complex light correction, methods such as gamma transform [19] and histogram equalization [20] are usually utilized. For instance, [PERSON] [21] proposed a wavelet transform based image segmentation method by combining gamma adjustments and transition region filters for image segmentation. Gamma transform is usually used to adjust the overall brightness of an image, but the conventional gamma transform takes the same treatment for all images, so the images of good quality are often overprocessed [22]. Over recent years, with the rapid development of computer vision, deep learning algorithms have become the mainstream in logo detection. CNN algorithms have been successfully applied in general object detection. In the field of logo detection, some scholars have proposed detection methods based on deep learning. For example, [PERSON] et al. [23] proposed a combination of convolutional neural network and histogram-of-gradients pyramid features for detecting and recognizing vehicle logos from images of the front and rear views of vehicles. [PERSON] et al. [24] proposed \"Regions with CNN\" (R-CNN), which has rich feature hierarchies for accurate object detection and semantic segmentation and used selective search (SS) [25] instead of the traditional sliding window method. [PERSON] et al. [26] proposed a method to improve the region proposal network (RPN) by appropriate anchor point selection to do the detection of logos and proposed a modified scheme by combining Faster R-CNN [27] and MobileNet [28], which has an impact on the high-resolution feature maps of mobile devices. Therefore, [PERSON] et al. [29] proposed a faster region-based convolutional neural network logo detection algorithm, which selectively extracts candidate frames to achieve multi-scale candidate frame classification.