Monday 03 March 2025
The quest for efficient object detection in autonomous vehicles has been an ongoing challenge for researchers and engineers. With the increasing demand for advanced driver-assistance systems (ADAS) and autonomous driving, the need to develop fast, accurate, and energy-efficient object detection algorithms has become more pressing than ever.
Recent advances in deep learning have led to significant improvements in object detection accuracy. However, these advancements come at a cost: increased computational complexity and memory usage, which can be detrimental to real-time performance on resource-constrained embedded platforms.
To tackle this challenge, researchers have been exploring various techniques for compressing neural networks while maintaining their original functionality. Pruning, quantization, and knowledge distillation are some of the popular methods used to reduce model size and computational requirements.
A new framework called UPAQ has been designed to address the efficiency-convergence trade-off in object detection. UPAQ combines semi-structured pattern pruning with quantization to achieve remarkable compression rates while maintaining high accuracy.
The team behind UPAQ experimented with two variants of the framework: UPAQ (HCK) and UPAQ (LCK). The former prioritizes higher compression ratios, sacrificing some accuracy for improved efficiency, while the latter focuses on achieving better performance at the cost of slightly lower compression.
Results from the experiments show that UPAQ outperforms state-of-the-art frameworks in terms of compression ratios, inference speed, and energy efficiency. For example, UPAQ (HCK) achieves a compression ratio of 5.62× for the PointPillars model, while UPAQ (LCK) delivers an mAP of 86.15%.
The framework’s performance is attributed to its ability to selectively prune unimportant weights and neurons, reducing computational costs during both compression and inference. This approach also enables adaptive rounding for weight quantization, allowing for more efficient use of memory resources.
One of the most promising aspects of UPAQ is its potential application in real-world scenarios. The team demonstrated the framework’s effectiveness on a Jetson Orin platform, which is commonly used in autonomous vehicles. This indicates that UPAQ could be easily integrated into existing systems without requiring significant hardware upgrades.
The development of UPAQ marks an important milestone in the pursuit of efficient object detection for ADAS and autonomous driving applications. As the automotive industry continues to evolve, the need for more accurate, faster, and energy-efficient algorithms will only continue to grow.
Cite this article: “Efficient Object Detection Framework UPAQ for Autonomous Vehicles”, The Science Archive, 2025.
Object Detection, Autonomous Vehicles, Deep Learning, Neural Networks, Pruning, Quantization, Knowledge Distillation, Compression, Inference Speed, Energy Efficiency
