Ruth V.

Responsibilities:

• Designed and implemented a modular real-time inference pipeline on the Jetson platform using DeepStream and GStreamer for agricultural computer-vision tasks.

• Integrated a PyTorch model, converted it to ONNX, and optimized it with TensorRT to run efficiently on the GPU with significantly reduced latency.

• Developed custom preprocessing and post-processing logic, including background-removal capabilities and frame-filtering conditions for improved detection clarity.

• Implemented frame-skipping and conditional processing strategies to maintain real-time performance under varying load and bandwidth conditions.

• Built asynchronous processing flows enabling inference, storage, and streaming operations to run concurrently without blocking the main pipeline.

• Added selective image-saving and integrated database logging mechanisms to store only relevant detection events while minimizing I/O overhead.

• Developed performance-benchmarking tools to measure GPU utilization, inference latency, throughput, and dropped-frame rates across multiple configurations.

• Set up a reproducible DeepStream development environment on the Jetson, including dockerized deployment, build scripts, and modular project structure.

• Conducted accuracy validation by comparing PyTorch, ONNX, and DeepStream outputs, ensuring numerical correctness throughout optimization stages.