The CNN-based feature extractor uses a pre-trained ResNet-50 model to extract features from images of the road surface. The input to the network is a 256x256 image of the road surface, and the output is a feature vector of dimension 128.
| Platform | Strength | Typical Stack | |----------|----------|---------------| | | Mature DAG visualisation, retry policies. | DockerOperator → autoplotter → road_estimator . | | Prefect Cloud | Serverless, easy Python‑first syntax. | @task decorators, async execution on Fargate. | | AWS Step Functions | Tight integration with S3, Lambda, Batch. | Lambda for vectorization, Batch for crack inference. | | Kubernetes (Kubeflow Pipelines) | Scalable GPU jobs, experiment tracking. | Pods: autoplotter-job , estimator-job . | autoplotter with road estimator crack
For city‑scale projects with mixed imagery sources, start with DeepCrack‑ResNet because it balances speed and accuracy (F‑score ≈ 0.88 on the RUT‑C dataset). The CNN-based feature extractor uses a pre-trained ResNet-50
Techno-thriller, with elements of innovation and entrepreneurship. | DockerOperator → autoplotter → road_estimator
