## 1 Introduction  
Light is one of the biggest factors that make it difficult to recognize the original shape of an object by lowering the object recognition rate. If the lighting conditions are bright or rough, the object may be blurred or overexposed, making it difficult to distinguish the object's features [1; 2; 3]. Additionally, shadows caused by increased contrast from light may obscure important information about an object's shape and size [4; 5]. Figure 1 is an example image showing problems caused by indoor and outdoor light. In the case of the chairs and apples, there are shadows or low-light areas on the objects depending on the location of sunlight or lighting, and in the case of the grapes, there are partially overexposed areas. Such phenomena inevitably occur where there is a luminous object, and this can make it difficult to guess or detect the exact size or number of objects, so methods or algorithms for improvement are needed. Additionally, these phenomena degrade image data quality, with the quality deteriorating more significantly outdoors than indoors. In particular, image contrast can have a significant impact on the performance of object recognition algorithms because it is determined by the amount of light [6].  
One of the most important tasks in computer vision is object recognition, a task that identifies and classifies objects within images (videos) [7]. Deep learning-based object recognition algorithms, such as convolutional neural networks (CNNs), have achieved state-of-the-art performance in object recognition tasks, and, more recently, models such as Vision Transformer (ViT) are also achieving state-of-the-art (SOTA) performance [8; 9]. These deep learning-based object recognition algorithms are highly dependent on the environmental factors which affect the quality of the training data, so model performance may deteriorate due to insufficient training data, large amounts of noise, and the presence of unlearned environmental factors [10; 11]. Therefore, it is important to make the environmental factors and quality of training data and input data the same [12].  
In addition, it is difficult to completely solve problems caused by lighting conditions, even with deep learning-based object recognition algorithms. Therefore, in object recognition tasks (classification, detection, segmentation), the preprocessing of training or input image data is necessary to improve recognition results for problem areas that appear according to the performance of the learning model and lighting conditions. In addition, deep learning technology that uses a lot of computing resources has emerged due to the development of big data and hardware devices such as CPUs, GPUs, and TPUs. However, image data improvement technology that uses deep learning algorithms has the following disadvantages: (1) overfitting problems due to lack of training data; (2) generalization performance degradation problems due to biases in training data; and (3) speed reduction problems due to high amounts of computation and memory usage [13; 14]. In particular, problems such as slowdown occur in unmanned vehicles, which require a small amount of computing resources [15].  
Therefore, in order to obtain improved results in the object recognition task by solving the problems caused by light or lighting conditions, this paper proposes a contrast enhancement-based preprocessing process. The proposed preprocessing process involves a method to improve images using statistical techniques which was devised to reduce the computational process. The proposed process suggests a way in which a similar environment can be built by improving the learning data and the input data; greater similarity between the learning data and the input data environment of the object recognition process will enable more accurate results to be obtained.