MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLO
This addresses real-time object detection for resource-constrained IoT devices like autonomous vehicles and security systems, though it appears incremental as it builds on existing YOLO models.
The paper tackles object detection in low-light and occluded IoT scenarios by introducing YOLO Phantom, which reduces parameters and model size by 43% and GFLOPs by 19% compared to YOLOv8n while maintaining comparable accuracy, achieving 17% and 14% FPS boosts for thermal and RGB detection.
Low-light conditions and occluded scenarios impede object detection in real-world Internet of Things (IoT) applications like autonomous vehicles and security systems. While advanced machine learning models strive for accuracy, their computational demands clash with the limitations of resource-constrained devices, hampering real-time performance. In our current research, we tackle this challenge, by introducing ``YOLO Phantom", one of the smallest YOLO models ever conceived. YOLO Phantom utilizes the novel Phantom Convolution block, achieving comparable accuracy to the latest YOLOv8n model while simultaneously reducing both parameters and model size by 43\%, resulting in a significant 19\% reduction in Giga Floating-Point Operations (GFLOPs). YOLO Phantom leverages transfer learning on our multimodal RGB-infrared dataset to address low-light and occlusion issues, equipping it with robust vision under adverse conditions. Its real-world efficacy is demonstrated on an IoT platform with advanced low-light and RGB cameras, seamlessly connecting to an AWS-based notification endpoint for efficient real-time object detection. Benchmarks reveal a substantial boost of 17\% and 14\% in frames per second (FPS) for thermal and RGB detection, respectively, compared to the baseline YOLOv8n model. For community contribution, both the code and the multimodal dataset are available on GitHub.