Smart Vision : Smart Object Detection for Robotics

Registration ID: IJNRD_324122

Published ID: IJNRD2604758

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Khushvendra Singh Tomar, Aman Kharwar, Aman kumar singh, Harsh Kumar, Lav Dixit

Object Detection, YOLO, Faster R-CNN, LiDAR, Deep Learning, Sensor Fusion, Radar, Machine, Real-Time Processing

Autonomous vehicles rely heavily on their ability to perceive and understand the surrounding environment in real time, making object detection a core component of safe and efficient driving systems. This paper explores how recent advances in object detection technologies—driven by deep learning, computer vision, and sensor fusion—are transforming the perception capabilities of autonomous vehicles. By examining widely used models such as YOLO, Faster R-CNN, and SSD, we highlight how these approaches balance accuracy and speed, and how their design makes them suitable for different driving scenarios, from highways to dense urban environments. Deep learning techniques, particularly convolutional neural networks (CNNs) and emerging transformer-based models, have significantly improved detection performance in complex environments. These models enable vehicles to identify and track multiple objects simultaneously, including pedestrians, vehicles, road signs, and unexpected obstacles. However, despite these improvements, several practical challenges persist. Conditions such as poor lighting, weather variations, and object occlusion can still reduce detection reliability. Additionally, the high computational demands of these models pose limitations for real-time deployment, especially on resource-constrained systems. To address these issues, this paper emphasizes the importance of sensor fusion, where data from cameras, LiDAR, and radar are combined to provide a more comprehensive understanding of the environment. By leveraging the strengths of each sensor type, fusion techniques can significantly improve detection accuracy and reduce false positives. We further discuss recent developments in multimodal learning, where models learn to process and integrate diverse data sources more effectively, leading to better decision-making in complex and dynamic driving conditions. Index Terms— Autonomous Vehicles, Object Detection, YOLO, CNNs, Sensor Fusion, Deep Learning, Real-Time Processing, LiDAR, Radar, Machine Learning, Edge AI, Adversarial Attacks, Smart Mobility.

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Khushvendra Singh Tomar, Aman Kharwar, Aman kumar singh, Harsh Kumar, & Lav Dixit (April-2026). Smart Vision : Smart Object Detection for Robotics. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), h441-h445. https://ijnrd.org/papers/IJNRD2604758.pdf

Volume 11 Issue 4, April-2026

Pages : h441-h445

Paper Reg. ID: IJNRD_324122

Published Paper Id: IJNRD2604758

Research Area: Other area not in list

Author Type: Indian Author

Country: Ghaziabad , Uttar Pradesh, India

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