Night Time Car Accident Prevention with AI Vision

Abstract

Night vision object detection with computer vision performs a vital function in diverse fields, when its dark, regular computer vision has trouble seeing things clearly. But because of new technology, we can now detect objects better in the dark. Our approach uses advanced learning methods and special sensors to help us see in darkness. By improving images, picking out important details, and using strong computer programs, we can spot objects accurately. After lots of testing, we\'ve found our system works really well, with few mistakes and it\'s fast too. It\'s useful in surveillance, self-driving cars, security, and rescue missions, making dark places safer. So, basically, our system is like giving night vision to computers. It\'s like putting on special glasses that help you see in the dark. With this technology, we can keep an eye on things even when it\'s hard to see, whether it\'s for keeping people safe or making sure our cars can drive themselves without crashing.

Introduction

I. INTRODUCTION

In places where it's dark or there's not much light, regular computer systems struggle to see objects clearly. But thanks to advancements like YOLO (You Only Look Once) or CNN (Convolutional Neural Networks), we're getting better at spotting things in the dark. This paper talks about combining computer vision with YOLO or CNN models to tackle this challenge. We use these advanced models and special sensors made for seeing in the dark. Our system helps us see objects better in low-light conditions. It makes images clearer and finds important details, even in the dark. Plus, it's smart enough to adjust to different lighting situations in real-time, making sure it works well no matter how dark it is. We've tested it a lot, and it's shown to be really accurate and safe in low-light places. It's useful in many areas like surveillance, self-driving cars, security, and rescue missions. Basically, using YOLO or CNN models changes how we detect objects at night, making it more accurate and safer in dimly lit places.

II. LITERATURE REVIEW

The literature on night vision object detection reflects a dynamic and evolving landscape, driven by advancements in imaging technologies, machine learning, and sensor integration. Researchers have explored diverse approaches to address the challenges posed by low-light conditions, contributing to the enhancement of safety and security in various applications.

Detection of objects in low-light or nighttime conditions is a tough task for regular computer vision systems. But thanks to recent progress in technology, especially in night vision and computer vision, researchers have been finding better ways to deal with this challenge.

One important area of research is combining computer vision methods with advanced deep learning techniques like convolutional neural networks (CNNs). These CNNs are really good at finding patterns and features, which makes them perfect for spotting objects in low-light situations. For example, a team led by Redmon in 2016 developed the YOLO (You Only Look Once) algorithm, which uses just one neural network to quickly and accurately detect objects, even when it's dark. Another big improvement comes from using special sensors like infrared sensors and thermal imaging devices. These sensors give us extra information about objects in the dark, which helps us see them better. A study by Amato and colleagues in 2020 explored combining visual and thermal data to make object detection even better in low light, and they found it made a big difference.

Making the images clearer before analysing them is also really important. Researchers like Chen and Li have come up with methods using deep learning and adaptive histogram equalization to make images sharper and improve object visibility, even when it's dark. Algorithms that can adjust to different lighting conditions is crucial.

These adaptive algorithms help the system respond to changes in the environment, like when the lighting or weather changes, ensuring it keeps working well no matter how dark it gets.

 A study by Wang and his team in 2017 proposed an adaptive thresholding technique that does just that, leading to better detection accuracy and reliability.

III. OBJECTIVE

A. Object Detection

At night, computer vision helps us see things better in low light, which is useful for surveillance and navigating in darkness. Special cameras, like infrared or thermal cameras, improve visibility. Smart computer programs analyze images to recognize objects, even in dim light, making us safer and more aware at night.

B. Real-Time Analyses

We use advanced computer programs to quickly analyze pictures taken at night. This helps us find objects in the dark more quickly and efficiently.

C. User-Friendly Interface

To make a user-friendly interface that's easy to use, with helpful features, so people can easily find objects in the dark using night vision.

IV. METHODOLOGY

This paper proposes creating strong computer models to recognize objects in night vision. We tried various methods like Faster R-CNN, MRCNN, YOLO V3, YOLO V8, SSD, and Retina Net. We found that YOLO, SSD, and Retina Net are effective for quickly spotting objects in thermal infrared images. We chose these because they work fast, making them suitable for tasks needing instant processing.

A. Data Collection and Pre-processing

For effective night vision object detection, it's crucial to gather a wide range of data that reflects real-world situations. Improving the quality of this data involves important steps like calibration and reducing noise. To make sure the system can detect objects accurately, we use techniques like rotation and scaling with the YOLO model. We also make sure our data has a fair mix of different types of objects. Separating the data for training and testing, especially with the YOLO model, greatly affects how well our system can detect objects in the dark.

B. Single Shot Detector (SSD)

When it comes to seeing things in the dark, the Single Shot Detector (SSD) is a great option. It's built to be fast and accurate, even when it's hard to see. SSD works quickly because it takes a straightforward approach, which is useful in fast-changing situations. It's particularly good at dealing with thermal infrared images, which are important for night vision. This makes it handy for tasks like surveillance or guiding vehicles in the dark. SSD is valuable in low-light conditions because it's fast, accurate, and can adapt quickly. Its ability to spot objects in the dark quickly makes it a useful tool for many tasks in challenging environments.

C. YOLOv8

In the realm of detecting things in the dark, YOLO v8 stands out as a top-notch model, surpassing its earlier versions. It's praised for its ability to quickly and accurately spot objects even when it's hard to see. YOLO v8 is particularly skilled at handling thermal infrared images, which are crucial for night vision. Its advanced design and technology make it highly accurate in real-time situations, adapting well to different lighting conditions, object sizes, and orientations. Using YOLO v8 shows a dedication to efficiency and accuracy, making it valuable for finding objects in challenging low-light conditions, especially for surveillance and guiding vehicles at night. night.

V. RESULT AND ANALYSIS

Night vision object detection with computer vision is like having a watchful guardian that comes to life when it's dark. It uses smart cameras that can see heat or detect low light. It's almost like having a special power that helps you navigate in the dark. This amazing technology silently watches over us, uncovering hidden dangers in the darkness and keeping our homes and important buildings safe. Imagine driving at night; this technology is like having extra eyes that can spot obstacles on dark roads, preventing accidents and keeping everyone safe. In military situations, it helps soldiers become skilled nighttime strategists, helping them find targets and move through the dark easily. Overall, it's like having a clever friend that keeps us safe and helps us work better in difficult low-light conditions, whether it's for home security, driving, or military missions.

VI. APPLICATIONS

AI vision technology has completely transformed various industries by acting as a versatile tool that enhances different areas. For instance, in self-driving cars, it acts as the "eyes" and "brain," helping cars understand their surroundings and drive safely. In stores, AI vision helps with security and inventory management by acting as a digital guard and analysing customer behaviour. Additionally, in medicine, it aids doctors in diagnosing diseases earlier and more accurately by quickly analysing medical images. In factories, AI vision ensures production quality by checking for errors on the production line. It also enhances virtual reality and augmented reality experiences, making them more immersive and enjoyable. Moreover, in agriculture, AI vision assists farmers in crop monitoring and decision-making processes. Lastly, it aids people with vision impairments by recognizing objects and providing helpful guidance for navigation. Overall, AI vision technology revolutionizes industries, making them more efficient, safer, and accessible for everyone.

VII. FUTURE SCOPE

Looking ahead, the future of night vision object detection is very exciting. It's not just about being able to see in the dark anymore – it's becoming something even more amazing. Imagine if it worked together with artificial intelligence, becoming really good at understanding details in low-light situations.

Picture this technology being used in cars to make driving at night much safer by helping drivers see and avoid obstacles on dark roads. And think about combining this with augmented reality, giving us new ways to understand and interact with the world in the dark, almost like having superpowers for seeing and understanding things at night. In the future, we might even see whole cities using night vision for better surveillance and faster emergency responses. It's not just about dealing with darkness anymore; it's about making our surroundings smarter, safer, and easier to see. The future of night vision object detection is going to change how we move around and explore the dark in really exciting ways.

Conclusion

Night vision object detection, similar to scenes from spy movies, is revolutionizing how we stay safe in dark environments. Using special cameras, it acts as a silent guardian, protecting homes and important places. Think of it as an extra set of eyes for drivers on poorly lit roads, providing unparalleled awareness and detecting obstacles in the dark. In the military, it\'s a hidden asset, aiding soldiers in nighttime navigation. Continuously advancing, it\'s not just about safety; it\'s about enhancing performance in low-light conditions. This intelligent companion appears poised to continually enhance our capabilities, unveiling previously unseen elements with its smart technology and meticulous planning.

References

[1] S. Liu, D. Huang, and Y.Wang, ``Receptive _eld block net for accurate and fast object detection,\'\' in Proc. Eur. Conf. Comput. Vis. Munich, Germany: Springer, 2018, pp. 385_400. [2] S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. T. H. Romeny, and J. B. Zimmerman, ``Adaptive histogram equalization and its variations,\'\' Comput. Vis., Graph., Image Process., vol. 39, no. 3, pp. 355_368, Sep. 1987. [3] K. He, J. Sun, and X. Tang, ``Single image haze removal using dark channel prior,\'\' IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341_2353, Dec. 2011. [4] H. Malm, M. Oskarsson, E. Warrant, P. Clarberg, J. Hasselgren, and C. Lejdfors, ``Adaptive enhancement and noise reduction in very low lightlevel video,\'\' in Proc. IEEE 11th Int. Conf. Comput. Vis., Rio de Janeiro, Brazil, Oct. 2007, pp. 1_8. [5] X. Dong, G. Wang, Y. Pang, W. Li, J. Wen, W. Meng, and Y. Lu, ``Fast efficient algorithm for enhancement of low lighting video,\'\' in Proc. IEEE Int. Conf. Multimedia Expo, Seven Springs, PA, USA, Jul. 2011, pp. 1_6. [6] Loza, Artur & Bull, David & Achim, Alin. (2010). Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients. Proceedings - International Conference on Image Processing, ICIP. 3553 - 3556. 10.1109/ICIP.2010.5651173. [7] S. Ren, K. He, R. Girshick, and J. Sun, ``Faster R-CNN: Towards real-time object detection with region proposal networks,\'\' in Proc. Adv. Neural Inf. Process. Syst., Montreal, QC, Canada, 2015, pp. 91_99. [8] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, ``SSD: Single shot multibox detector,\'\' in Proc. Eur. ConfComput. Vis. Amsterdam, The Netherlands: Springer, 2016, pp. 21_37. [9] Y. P. Loh and C. S. Chan, ``Getting to know low-light images with the exclusively dark dataset,\'\' Comput. Vis. Image Understand., vol. 178, pp. 30_42, Jan. 2019.

Copyright

Copyright © 2024 Yash Aggarwal, Vikash , Vaibhav Kumar, Pranjal , Shuchi Sharma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.