Optimizing Real-Time Object Detection on Edge Devices: A Transfer Learning Approach
Keywords:
K-Means, DNN , IoT, Video Surveillance, edge computing, tinyMLAbstract
The issue of object detection in remote surveillance using edge devices presents a complex scenario, largely as a result of the limitations inherent in edge computing settings and the requirements for instantaneous data processing. Current video surveillance systems exhibit proficient video capture functionalities; however, data analysis at the server level is impeded by constraints in transmission power and the availability of cloud computing resources. Consequently, Internet of Things (IoT) devices are primarily relegated to the role of data acquisition. Our study proposes a novel fusion of ResNet18, K-Means clustering, and int8 quantization over tinyML, compressing the model to <100KB and enabling sub-1mW consumption on edge devices. This methodology extends the viability of deploying sophisticated machine-learning models on microcontrollers powered by coin cells, broadening their applicability in various settings for object detection. Employing int8 quantization, our model attains a notable improvement in latency by 45%, coupled with a 70% reduction in RAM consumption and a 65% decrease in flash storage. This research delves into the significance of optimization in the process of choosing latency-efficient deep neural network (DNN) models for various edge computing configurations, emphasizing the delicate balance between hardware capabilities and optimization approaches. Subsequent research efforts will concentrate on refining quantization algorithms to further mitigate the precision differential in computational models.
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