Streamlining Object Detection with Fuzzy Logic and Fast R-CNN: Enhancing Image Quality and Classification Accuracy

Problem Definition

From the literature review conducted, it is evident that existing object detection models face several limitations and challenges in their performance. While these models have been instrumental in various applications such as traffic management, face recognition, and pedestrian detection, they struggle when faced with visual issues such as noise, low contrast, and low brightness in images. The use of deep learning algorithms has enabled these models to handle large datasets effectively during training. However, the time taken for training these models is significantly high, leading to complexity and reduced efficiency. As a result, there is a pressing need for a new object detection model that can address these challenges and limitations.

This new model should be capable of handling visual problems in images, such as low contrast and brightness, while maintaining high accuracy in object detection. By developing a more robust and efficient object detection model, researchers can overcome the existing limitations and pave the way for improved object recognition in various real-world applications.

Objective

The objective of this study is to address the limitations and challenges faced by existing object detection models by proposing an improved approach based on fuzzy logic. This new model aims to effectively detect and identify objects in images by utilizing proper image processing techniques, such as contrast and brightness enhancement using CLAHE and BBHE. By incorporating a fuzzy decision model to differentiate between normal and affected images, the proposed approach seeks to enhance object detection accuracy and efficiency. The study also employs the Fast-RCNN model for classifying objects in the images, which has shown effectiveness in various computer vision applications. Overall, the objective is to develop a more robust and efficient object detection model that can handle visual issues in images while maintaining high accuracy in object recognition for real-world applications.

Proposed Work

In order to overcome drawbacks of classic object detection models, an improved approach based on fuzzy system is proposed in this paper. The main motivation of this model is to effectively detect and identify the various objects present in affected images by applying the proper image processing technique that refines the inputs before passing it to the detection model. The images that are captured normally under good lighting source didn’t need to go for pre-processing. While as, the images with low contrast and brightness needs pre-processing before passing them to classifier. Now, the question is how to identify which input image is normal and which is affected one.

To do so in an effective way, the suggested approach would make use of a fuzzy decision model to aid in the detection of normal and affected images. The main motive of using the fuzzy logic in the proposed work is because it is straightforward, incredibly simple framework that can efficiently control machines and provides effective results in decision making. The suggested approach employs a Mamdani type of FIS that takes contrast and brightness as two inputs. Moreover, to enhance the contrast and brightness of the affected images, the proposed model is utilizing the contrast limited adaptive histogram equalization (CLAHE) approach and Brightness preserving Bi-Histogram equalization (BBHE) techniques. CLAHE is a useful approach for enhancing the contrast of local images that has proven to be effective and beneficial in a variety of situations.

It is widely employed in computer vision and pattern identification technologies to improve visual contrast. Whereas, BBHE splits the histogram at input side in two sections on the basis of its mean brightness which are then equalized independently into two sub-histograms. According to experts, once the source histogram has a quasi-symmetrical dispersion close to its mean value, BBHE can retain its native brightness up to a specific level. Furthermore, the suggested study employs the Fast-RCNN model for classifying objects in the images. Fast-RCNN was firstly developed by Ross Girshick, Shaoqing Ren, Kaiming He, and Jian Sun in the year 2015 that works effectively in majority of the computer vision applications.

Application Area for Industry

This project can be valuable in various industrial sectors such as transportation, surveillance, healthcare, and manufacturing. In transportation, the improved object detection model can be used for traffic management to detect vehicles and pedestrians accurately, even in challenging visual conditions. In the surveillance industry, the model can help in identifying objects and individuals with precision, enhancing security measures. In healthcare, the model can assist in medical imaging for identifying and analyzing specific areas of interest. Furthermore, in manufacturing, the model can be utilized for quality control to detect defects in products during the production process.

By addressing the challenges of low contrast and brightness in images, the proposed solutions in this project can significantly improve object detection accuracy and efficiency in various industrial domains. The integration of the fuzzy decision model, CLAHE, BBHE techniques, and Fast-RCNN classifier offers a comprehensive approach to enhancing object detection performance, ultimately leading to better results, reduced complexity, and increased productivity in industrial applications.

Application Area for Academics

The proposed project can significantly enrich academic research, education, and training in the field of computer vision and object detection. By introducing a novel approach that combines fuzzy logic with image processing techniques like CLAHE and BBHE, researchers, MTech students, and PHD scholars can explore innovative research methods for enhancing object detection in images affected by low contrast and brightness. This project's relevance lies in addressing the limitations of existing object detection models and providing a more effective solution for detecting objects in challenging visual conditions. The integration of fuzzy decision models and advanced image processing techniques opens up new possibilities for improving detection accuracy and efficiency, especially in real-world applications where images may not always have ideal lighting conditions. Researchers in the field of computer vision can utilize the code and literature from this project to further investigate the potential applications of fuzzy logic in object detection and explore ways to optimize image processing techniques for better detection results.

MTech students and PHD scholars can also benefit from studying the methodologies and algorithms used in this project to enhance their research skills and develop new approaches for solving similar challenges in the domain of computer vision. Moreover, the application of the Fast-RCNN model for classifying objects in images further enhances the project's potential for advancing research in computer vision and machine learning. By combining cutting-edge technologies and research domains, this project offers a platform for exploring innovative research methods, conducting simulations, and analyzing data to push the boundaries of object detection capabilities. In conclusion, the proposed project not only enriches academic research by introducing a new approach to object detection but also provides a valuable resource for educational and training purposes in the field of computer vision. The utilization of advanced technologies and research methodologies in this project opens up opportunities for future research endeavors and the development of more efficient and accurate object detection models.

Algorithms Used

The proposed work in this project utilizes a combination of BBHE, Fuzzy Logic, CLAHE, and Faster RCNN algorithms to enhance the accuracy of object detection in images. BBHE and CLAHE are used to improve the contrast and brightness of affected images before passing them to the detection model. Fuzzy logic is employed to distinguish between normal and affected images by creating a decision model based on contrast and brightness inputs. The Fast-RCNN model is then utilized for classifying objects in the images, providing efficient and effective results for object detection tasks.

Keywords

object detection, fuzzy logic, image enhancement, brightness preserving bi-histogram equalization, BBHE, contrast-limited adaptive histogram equalization, CLAHE, Fast-RCNN, deep learning, image processing, decision-making, image quality enhancement, computer vision, feature extraction, image segmentation, edge detection, image preprocessing, convolutional neural networks, CNNs, object localization, image recognition, image analysis

SEO Tags

object detection, fuzzy logic, image enhancement, BBHE, CLAHE, Fast-RCNN, deep learning, image processing, decision-making, computer vision, feature extraction, image segmentation, edge detection, image preprocessing, CNNs, object localization, image recognition, image analysis