vehicle recognisation using deep learning cnn.pptx

Vehicles are machines designed to transport people or cargo, and their evolution has been a primary driver of human progress, fundamentally reshaping societies, economies, and the very geography of our world. From the first wheel to the complex intelligent systems of today, vehicles represent humanity's enduring quest for mobility and connection.

### The Dawn of Mechanical Motion
For millennia, transportation was limited by the power of muscle, whether human or animal. The true revolution began with the Industrial Revolution and the harnessing of steam power, which gave rise to the first locomotives and steamboats. However, it was the development of the **internal combustion engine** (ICE) in the late 19th century that truly untethered humanity. Pioneers like Karl Benz and Henry Ford did not just invent a machine; they created a new paradigm of personal freedom. Ford's implementation of the moving assembly line made the automobile accessible to the masses, triggering a cascade of societal changes, including the growth of suburbs, the creation of vast road networks, and the birth of countless new industries.

### A World on Wheels
The concept of a vehicle extends far beyond the personal car. The specialization of vehicles has allowed humanity to conquer land, sea, and air, each designed for a specific purpose and environment.

* **Land Vehicles:** This is the most diverse category, including passenger cars, motorcycles for nimble transport, buses for public transit, and trucks that form the backbone of modern logistics. Trains continue to be a vital mode of transport for both passengers and heavy freight over long distances.
* **Watercraft:** Ships and boats have been essential for trade and exploration for centuries. Modern watercraft range from massive container ships and oil tankers that carry global trade to sophisticated submarines and personal leisure boats.
* **Aircraft:** The 20th century saw humanity take to the skies. Airplanes have made the world smaller, enabling rapid long-distance travel and air freight. Helicopters offer unique vertical takeoff and landing capabilities, crucial for emergency services, construction, and military operations.
* **Specialized Vehicles:** This category includes everything from agricultural tractors and combines that feed the world to heavy construction equipment that builds our cities. At the pinnacle of vehicle engineering are spacecraft, which have carried humans to the Moon and probes to the far reaches of our solar system.

### The Digital Transformation
In recent decades, vehicles have transformed from purely mechanical constructs into sophisticated electronic systems on wheels. The integration of computers began with engine control units (ECUs) to optimize fuel efficiency and reduce emissions. This digital foundation has paved the way for a new era of automotive technology defined by safety, connectivity, and autonomy.

Modern vehicles are equipped with an array of advanced driver-assistan

1. Detection and Classification using Deep Models Presented By Komal Vishnu Venkata Reddy

2. Outline Introduction Challenges Research Gaps Research Works Research Ongoing Conclusion

3. Introduction Automatic vehicle detection and classification (AVDC) systems have become important for real-time traffic monitoring and management. AVDC requires collection of suitable data with real time traffic information, and automatic vehicle classification and detection methodologies. Fig 1. Vehicle Detection

4. Research Challenges

5. Research Gaps Many datasets used over the years give almost 100% accuracy. Huge samples are needed to train deep learning based models. Many datasets are not accurately processed and annotated . In Indian sub-continent road conditions, vehicle types, traffic scenarios are dissimilar than those found in developed countries. Hence, most of the research articles available in the literature may not be suitable for such cases. Some datasets have very few classes.

6. Last Decade in Vehicle Detection and Classification: A Comprehensive Survey The current survey encompasses all key research papers published on AVD between the year 2010 and 2020. It gives a general overview of the several approaches applied for both localizing as well as recognizing different vehicle classes using both machine learning and deep learning approaches. It includes the most widely used AVD datasets (comprising of video as well as still-image datasets) used for both vehicle localization as well as classification problems. It also presents a critical review of the deep learning based methods, which is the current trend, used for AVD problem. Finally, it analyses different prospects of future research achievements in this field Published in Archives of Computational Methods in Engineering -Springer

7. Two decades of vehicle make and model recognition – Survey, challenges and future directions To the best of our knowledge, this paper discusses most of the research papers on VMMR published between the year 2004 and 2023. We have identified as well as provided a general overview of various methodologies for VMMR based on both machine learning and deep learning models supported by some in-depth discussion. We have provided information regarding several vehicle related datasets used for VMMR task. Finally, we have presented various research gaps, and their potential solutions for the said field’s research advancements in the future. Published in Journal of King Saud University - Computer and Information Sciences - S ciencedirect -Elsevier

8. Current Datasets and Their Inherent Challenges for Automatic Vehicle Classification Also, we have given a comparative study of the different types of datasets used for classification along with their pros and cons. This study presents a comprehensive survey of the datasets available for AVC and vehicle model and make recognition (VMMR) published in the last 10 years highlighting their inherent challenges. Published in Machine Learning for Cyber Physical System: Advances and Challenges -Springer

9. Performance Comparison of Various YOLO Models for Vehicle Detection: An Experimental Study In this paper, we focus on three major object detection algorithms under the YOLO family, namely YOLOv5, YOLOv7, and YOLOv8 for the purpose of vehicle detection, D iscuss the architectural differences of these variants. P erformance comparison of these models, and in doing so, we use two recently introduced AVD datasets developed for the Indian subcontinent, namely JUVDsi v1 and IRUVD. Published in Proceedings of Data Analytics and Management- ICDAM 2023, Volume 3 Springer

10. JUVDsi v1: developing and benchmarking a new still image database in Indian scenario for automatic vehicle detection The image database is properly annotated to measure the performance of any algorithm developed for automatic localization and classification of vehicles in an unconstrained environment. Different complexities are added to the images to make it challenging as well as realistic. Nine different classes of vehicles are presented in this database – very few of the existing databases have considered such a variety of vehicle classes. T hree models namely, You Only Look Once (YOLO) , Region-Based Convolutional Neural Networks (R-CNN) , and Region-Based Fully Convolutional Networks (RFCN) are used. Finally an ensemble method, called Weighted Boxes Fusion (WBF) is implemented. Published in Multimedia Tools and Applications, Springer

11. JUIVCDv1: development of a still-image based dataset for indian vehicle classification This dataset offers a realistic image representation of the traffic situation in India, which isbvery different from that of other developed countries. Vehicle images captured in variousbscenarios are considered. A total number of 6335 vehicle images can be found in this dataset.. Researchers may take this dataset to evaluate the effectiveness of their methods for autonomous vehicle localization and categorization. The vehicle images in the collection are taken in different weather conditions. Therefore, the model is resilient enough to handle data collected in a variety of meteorological scenarios. W e have benchmarked this dataset using an MVE classifier combination approach which achieves 95% accuracy. Published in Multimedia Tools and Applications, Springer

12. XMR_Net: A Deep Model for Vehicle Make and Model Recognition using Still-images In this work, we have proposed an ensemble of attention-aided three deep CNN models, called XMR_Net, for VMMR. In this paper, initially, we have used five standard convolutional neural network (CNN) models, namely Inceptionv3, Xception, InceptionResNetv2, MobileNetV2, and ResNet152v2 for VMMR. We have also used an attention mechanism to these models. To increase accuracy of the overall model we have chosen three best base learners from these five CNN models, and formed an ensemble model. The final model is called XMR_Net

13. SimSANet: A Simple Sequential Attention aided Deep Neural Network for Vehicle Make and Model Recognition We present Simple Sequential Attention Network ( SimSANet ), a multikernel -based sequential attention-based model, which efficiently extracts the most discriminative information by combining both global and local features. It also offers significant advantages in speed, effectiveness, and efficiency, requiring far fewer parameters compared to existing models. To demonstrate the significance of each layer in the architecture of the suggested model, along with the recommended values for the hyperparameters, we perform a statistical analysis and ablation studies. We utilize the Grad-CAM to show the efficacy of the proposed model. We conduct extensive experiments on multiple public VMMR benchmark datasets to ensure the effectiveness of the proposed model.

14. A Feature Fusion based Custom Deep Learning Model for Vehicle Make and Model Recognition Deep Feature Fusion: It fuses two feature maps that are extracted from input images in two parallel paths containing two different base models. Modified CBAM: Taking inspiration from the original CBAM attention [14], a modified attention mechanism is used, which extracts channel and spatial information in parallel and combines them with input feature maps. Generalized Approach: Our model is evaluated on two VMMR datasets, namely Stanford Cars and Comp-CarsSV, and achieved 93.51% and 99.03% test accuracies, respectively.

15. Research Ongoing Developing vehicle detection dataset for adverse weather condition. Developing a deep learning based vehicle detection lightweight model.

16. RESULTS . High-Accuracy Classification via Transfer Learning:  The core of the project is a deep learning model that classifies vehicle images. Instead of building a model from scratch, we used  transfer learning  with Google's  MobileNetV2 , a state-of-the-art Convolutional Neural Network (CNN). This allowed us to leverage a model pre-trained on millions of images, leading to faster training and higher accuracy on our specific task of identifying bikes, buses, cars, and trucks. Robust Web Application with Flask:  The trained model was deployed in a web application built with the Python  Flask  framework. The backend handles image uploads, preprocesses them to the required 224x224 pixel size, and uses the saved vehicle_model.h5 file to make predictions. This creates a practical and user-friendly interface for the AI model.

17. Result An image recognition model identifies a truck by detecting its most defining characteristics, primarily its large, two-part structure consisting of a distinct cab and a separate, long cargo area. It also recognizes key local features like high ground clearance, a large vertical front grille, and multiple sets of wheels, which differentiate it from smaller cars or single-body buses.

18. Conclusion

19. Thank You