## 5 Conclusions  
In this paper, we proposed a contrast enhancement-based preprocessing process which uses the CLAHE algorithm to obtain improved results in object tasks by improving problem areas caused by light or lighting conditions. The preprocessing process extracts the optimal 'cliplimit' and 'additional brightness (\(\alpha\))' values, generates optimal image data based on these, and then proceeds with the quality and similarity measurement process.  
The optimal values refer to the absolute brightness difference between the two images (original and contrast-enhanced) and the maximum value of the variance for sharpness, respectively. The generated image has optimal contrast enhancement and brightness control, making it more suitable and easier to use for object recognition than the original image, and it has a pixel environment more similar to that of the original image than those of the images generated through most contrast enhancement algorithms. Deep learning models used in object recognition tasks rely heavily on learning data, and environmental similarities between learning data and input data can also affect performance. Therefore, the proposed preprocessing process can be used not only for generating learning data, but also as a preprocessing process for input data in the object task process, and experiments were conducted by applying the preprocessing process to learning and input data. Object tasks include classification, detection, and segmentation, and CNN, YOLOv5, and YOLACT models were used for these, respectively. The X-ray, apple, grape, and marine deposited waste datasets were selected as training datasets based on the need for technology to improve contrast. As a result of the experiment, the performance of the original datasets was high in three tasks. However, when comparing the object task performance of the original dataset with that of the contrast-enhanced datasets to which this paper's preprocessing process was  
Figure 16: Images of fish net instance segmentation results.  
applied, improvements were observed in areas such as shadows or contrast phenomena while the existing performance was maintained or improved. In the classification task, the average accuracy was slightly improved, and in the object detection task, additional objects were detected in shadows or low-light areas and the detection rate was improved. In the segmentation task, the size of the object was segmented more accurately than in the original dataset. Through this, it will be possible to improve results not only for the dataset used in this paper, but also for datasets that require contrast enhancement. The proposed preprocessing process builds a similar environment through improved learning data and input data in the object recognition task process, and it suggests that more accurate results can be obtained.  
In future research, we plan to study preprocessing process optimization and application methods that can be applied to hardware that requires low resources, such as robots and unmanned vehicles.  
Conceptualization, writing--original draft preparation, methodology, software, data curation, investigation, T.-s.W.; visualization, formal analysis, validation, T.-s.W. and G.T.K.; resources, project administration, M.K. and J.J.; writing--review and editing, supervision, funding acquisition, J.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center Support Program (IITP-2023-2016-0-00318), and it was supervised by the IITP (Institute for Information and Communications Technology, Planning, and Evaluation).  
The authors declare no conflict of interest.