# Detection of Logos of Moving Vehicles under Complex Lighting Conditions
## 1 Introduction  
The detection and classification of image-based vehicle information is an important branch of intelligent transportation systems [1]. As important information on vehicles, their logos are notable and difficult to replace, and correct detection of vehicle logos is helpful for the identification of vehicles [2]. The accurate and rapid identification of vehicle logos has a wide range of applications in vehicle query, vehicle escape, and other aspects; therefore, logo detection and classification are of great significance [3]. When a vehicle is driven on a road, cameras are susceptible to weather conditions when capturing images because the size of vehicle logos in such images is generally not large enough. Generally, traditional research on vehicle logo recognition technology is divided into two parts: positioning and recognition [4]. Vehicle logo detection systems may have a good recognition rate in simple environments with even lighting; however, in the case of a complex environment, dark weather, or uneven lighting, traditional vehicle logo detection systems may hardly reach the standard of practical application. Therefore, in order to recognize vehicle logos on the road, it is necessary to first solve the problem with the influence of complex lighting environments on image detection and then recognize vehicle logos with the help of image enhancement [5]. Image processing methods for complex light conditions mainly include frequency domain processing and space domain processing [6]. Gamma theory [7] and Retinex theory [8] are commonly adopted in spatial domain-based image enhancement methods, and after decades of development, multi-scale image enhancement algorithms [9], etc., have gradually emerged. For automatic recognition of vehicle logos in a complex light environment, adaptive image enhancement algorithms are needed; however, conventional algorithms generally show the disadvantage of halo artifacts at image edges, where the image pixels would change drastically, with wide color distortion. So far, none of the improved methods can satisfy the requirement of adaptive enhancement of complex light [6]. Algorithms based on deep learning have been implemented in the field of logo detection and recognition, with good results achieved [10]. Convolutional Neural Networks (CNNs) [11] can improve the detection rate of vehicle markings by reconstructing the target detection task as a regression issue from image pixels directly to bounding box coordinates and class probabilities. However, in such scenarios as localization, jitters from the motion of the detection devices would result in lower detection accuracy in a moving environment. In addition, when a mature deep learning framework is used to detect logos, there is a problem of missed detection if deep neural network learning is implemented due to different sizes of logos in different images. When the detection target is a moving vehicle, the detection method should also quickly do recognition of the vehicle logo. This is the reason why traditional methods perform automotive logo detection by first locating and then detecting such logos [2]. To address the above issues, this study proposes a method for vehicle logo detection and recognition under complex lighting conditions. Firstly, an adaptive image enhancement algorithm is designed to adjust the brightness and contrast of images so as to reduce the leakage rate in subsequent detection; secondly, a target detection algorithm is improved, and a network structure is designed for the multi-scale problem of vehicle logos in different images; finally, an optimization method is improved to make a balance between the accuracy and speed of vehicle logo detection.