iTaskSense: Task-Oriented Object Detection in Resource-Constrained Environments

Task-oriented object detection is increasingly essential for intelligent sensing applications, enabling AI systems to operate autonomously in complex, real-world environments such as autonomous driving, healthcare, and industrial automation. Conventional models often struggle with generalization, requiring vast datasets to accurately detect objects within diverse contexts. In this work, we introduce iTask, a taskoriented object detection framework that leverages large language models (LLMs) to generalize efficiently from limited samples by generating an abstract knowledge graph. This graph encapsulates essential task attributes, allowing iTask to identify objects based on high-level characteristics rather than extensive data, making it possible to adapt to complex mission requirements with minimal samples. iTask addresses the challenges of high computational cost and resource limitations in vision-language models by offering two configuration models: a distilled, task-specific vision transformer optimized for high accuracy in defined tasks, and a quantized version of the model for broader applicability across multiple tasks. Additionally, we designed a hardware acceleration circuit to support real-time processing, essential for edge devices that require low latency and efficient task execution. Our evaluations show that the task-specific configuration achieves a 15% higher accuracy over the quantized configuration in specific scenarios, while the quantized model provides robust multi-task performance. The hardware-accelerated iTask system achieves a $3.5 x$ speedup and a 40% reduction in energy consumption compared to GPU-based implementations. These results demonstrate that iTask’s dual-configuration approach and situational adaptability offer a scalable solution for task-specific object detection, providing robust and efficient performance in resourceconstrained environments.