Real-time Object Detection on Raspberry Pi 4 : Fine-tuning a SSD model using Tensorflow and Web Scraping

Abstract: Edge AI is a growing area. The use of deep learning on low cost machines, such as the Raspberry Pi, may be used more than ever due to the easy use, availability, and high performance. A quantized pretrained SSD object detection model was deployed to a Raspberry Pi 4 B to evaluate if the throughput is sufficient for doing real-time object recognition. With input size of 300x300, an inference time of 185 ms was obtained. This is an improvement as of the previous model; Raspberry Pi 3 B+, 238 ms with a input size of 96x96 which was obtained in a related study. Using a lightweight model is for the benefit of higher throughput as a trade-off for lower accuracy. To compensate for the loss of accuracy, using transfer learning and tensorflow, a custom object detection model has been trained by fine-tuning a pretrained SSD model. The fine-tuned model was trained on images scraped from the web with people in winter landscape. The pretrained model was trained to detect different objects, including people in various environments. Predictions shows that the custom model performs significantly better doing detections on people in snow. The conclusion from this is that web scraping can be used for fine-tuning a model. However, the images scraped is of bad quality and therefore it is important to thoroughly clean and select which images that is suitable to keep, given a specific application.