Object Detection on Resource-Constrained Platforms Using a Configurable Ensemble of Detectors

Object detection from high resolution images is increasingly used for many important application areas of defense and commercial sensing. However, object detection on high resolution images requires intensive computation, which makes it challenging to apply on resource-constrained platforms such as in edge-cloud deployments. In this work, we present a novel system for streamlined object detection on edge-cloud platforms. The system integrates multiple object detectors into an ensemble to improve detection accuracy and robustness. The subset of object detectors that is active in the ensemble can be changed dynamically to provide adaptively adjusted trade-offs among object detection accuracy, real-time performance, and energy consumption. Such adaptivity can be of great utility for resource-constrained deployment to edge-cloud environments, where the execution time and energy cost of full-accuracy processing may be excessive if utilized all of the time. To promote efficient and reliable implementation on resource-constrained devices, the proposed system design employs principles of signal processing oriented dataflow modeling along with pipelining of dataflow subsystems and integration on top of optimized, off-the-shelf software components for lower level processing. The effectiveness of the proposed object detection system is demonstrated through extensive experiments involving the Unmanned Aerial Vehicle Benchmark and KITTI Vision Benchmark Suite. While the proposed system is developed for the specific problem of object detection, we envision that the underlying design methodology, which integrates adaptive ensemble processing with dataflow modeling and optimized lower level libraries, is applicable to a wide range of applications in defense and commercial sensing.