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.

翻译：低光照条件和遮挡场景会阻碍物联网（IoT）在实际应用（如自动驾驶车辆和安全系统）中的目标检测。尽管先进的机器学习模型追求高精度，但其计算需求与资源受限设备的局限性相冲突，从而影响实时性能。在本研究中，我们通过引入"YOLO Phantom"（有史以来设计的最小YOLO模型之一）来应对这一挑战。YOLO Phantom 利用新型幻卷积模块，在实现与最新YOLOv8n模型相当精度的同时，将参数和模型大小均减少了43%，导致十亿次浮点运算次数（GFLOPs）显著降低19%。YOLO Phantom 利用基于多模态RGB-红外数据集的迁移学习来解决低光照和遮挡问题，使其在恶劣条件下具备鲁棒视觉能力。其实用效果在配备先进低光照和RGB摄像头的物联网平台上得到验证，该平台无缝连接至基于AWS的通知端点，以实现高效的实时目标检测。基准测试表明，与基线YOLOv8n模型相比，热成像和RGB检测的每秒帧数（FPS）分别显著提升了17%和14%。为促进社区贡献，相关代码及多模态数据集已发布于GitHub。