Project Proposal

1. High-level project introduction and performance expectation

The proposed work focuses to develop an efficient practical surveillance and security systems to harness the power of real-time adaptive vehicle systems. The objective of the mode is to assess the perceptual image quality and recovering it from corrupted image frames in adaptive Vehicle systems for effective realization. The ideology is to Customize Processor design for reducing the programming complexity along with memory management. The implementation of the image processing algorithm with the added advantage of Field Programmable Gate Array (FPGA) for hardware computational architecture to optimize the latency and high throughput computational operation. This will be much needed and efficient model for Adaptive Vehicle systems.

The intricate focus lies on a mathematical understanding of how deep learning techniques can be employed for image reconstruction tasks, and how they can be connected to traditional approaches to solving practical security applications for vehicle systems. The overall implementation of the proposed model reflects the diversity of approaches in applying FPGAs to image processing applications.

In the future, the proposed methodology can be extended to SLVs and military applications with improved accuracy and speed by incorporating other state-of-the-art techniques and optimized hardware architectures.

2. Block Diagram

3. Expected sustainability results, projected resource savings

Designing systems that can adapt themselves to changing environmental conditions is of rising interest for industry and especially for the automotive industry considering autonomous driving. Modern cars are equipped with powerful computational resources for autonomous driving systems (ADS) as one of their key parts to provide safer travels on roads. High accuracy, processing speed, and real-time requirements of ADS are addressed by HW/SW co-design methodology which helps in connecting the computationally intensive tasks efficiently to the hardware part. An innovative approach to the ADS for the reconstruction of real-time images has been proposed in this model. The proposed architecture aims to implement image processing algorithms on FPGA and reconstruct it based on the requirement. The reconstructed image can be connected to any display system using VGA interface and the reconstructed image can be displayed. Based on this, the proposed system can be configured to help the vehicle systems to reconstruct the images at high speeds and in a hardware efficient way. 

Image processing using FPGA is one of the prime areas of research that has become popular these days. This is mainly because of the higher operational speed along with improved and accurate processing. In Adaptive systems, speed plays a vital role and hence the FPGAs have gained importance in this area these days.

In image processing applications, memory management plays an important role. Optimizing the memory usage and managing it efficiently will lead to a new and efficient system with higher accuracy and reliability.

The main focus of the proposed work is to reconstruct an image for the vehicle system, improve the speed of operation, reduce hardware utility and introduce better memory management.

4. Design Introduction

The proposed design receives a real-time image from a camera device and based on the computational algorithm, the image is reconstructed and interfaced to a display device.

In this proposal Image Processsing Toolbox is used for preprocessing and acquiring best image data provided by the interactive workflows such as segmenting image data, comparing image registration techniques, and batch process large data sets.

Featuring embedded Altera DE10-Nano FPGA Development kit will be most effective for regressive image realization and for machine vision camera operations of the proposed model. Further VGA interfacing is best utilized with help of Intel FPGA for display in the screen. 

Military applications, drones, unmanned surveillance devices, SLVs, etc are the targeted users.

5. Functional description and implementation

Machine Learning has now become the most widely used method in any hardware application and adaptive autonomous vehicle systems is certainly one among them. ML algorithms assist in detecting objects, images aided by surveillance security systems and mimicking human visual cues and interactions.

ML Algorithms typically require a certain amount of high-quality datasets to process and predict highly accurate results. Hence Computers use input images acquired from proposed architecture and convert into an array of pixels according to frame resolutions and store them in an array of a matrix of RGB features. These data features are combined to build a machine-learning algorithm to classify an extensive database of feature vectors whose classification is known.

Image Processing workflow with machine learning model :

In-vehicle systems, the runtime faults should be avoided as it may cause serious effects. So precise and fast FPGA architectures are employed in the proposed model using machine learning analysis.

6. Performance metrics, performance to expectation

The most important to preferring the intel board and software, with the low cost and high-performance adaptable architecture is easily configurable with other hardware architecture.

Benifit of using Intel FPGA is that the low utility of hardware modules for big data image realization algorithm. Also the block ram memory reading is best utlilized for reading big data camera vison image pixels.

7. Sustainability results, resource savings achieved

8. Conclusion

The main focus in this proposed work is to reconstruct an image for the vehicle system, improve speed of operation and managing it efficiently will lead to a new and efficient system with higher accuracy and reliability.

This proposed model can be further utilized as a base for Military applications, drones and automotive surveillance systems.