Object Detection API Deployment: Android OS Platforms

The rationale of this project-investigation is grounded in versatility and the universal nature of an pretrained object API neural network model. More importantly, after a review of disease burdens in regions and countries of South Africa, a standard of accessibility must be attained to allow rapid handheld usage. The levels of Tensorflow API usage are defined by the interactivity of the application itself. The project ultimately moved from a stage of moderate-low complexity (a Python shell console that required necessary path directory modifications) to a fully automated application that used a preloaded frozen_inference_graph.py to recognize novel images. The SSD Mobilenet V1 model was the sole candidate in performing real time 30 FPS rate object detection due to its compressed size and capacity. Figure 2.4 reveals the Android application detecting two Syphilis cells in a blood-specimen culture: