An iOS image-processing app for MAHLE that measures cooling-fin density on components using the device camera. The user captures and crops an image of the finned surface, and pixel-based detection counts the fins over a measured distance to compute fins count, total distance and fins density - a dimensional inspection metric for heat-exchanger / radiator parts.
An iOS image-processing app for MAHLE that measures cooling-fin density on components using the device camera. The user captures and crops an image of the finned surface, and pixel-based detection counts the fins over a measured distance to compute fins count, total distance and fins density - a dimensional inspection metric for heat-exchanger / radiator parts.
Built the FinDensity iOS app for MAHLE. Developed a custom AVCapture camera screen with focus-square framing and image cropping, an Objective-C ImageProcessor (OpenCV-backed) that runs pixel-level analysis to detect and count fins on the captured surface, and a ScanResult screen that reports fins count, total distance and fins density, with part-number tagging and history saved via an FMDB SQLite database.
An iOS image-processing app for MAHLE that measures cooling-fin density on components using the device camera. The user captures and crops an image of the finned surface, and pixel-based detection counts the fins over a measured distance to compute fins count, total distance and fins density - a dimensional inspection metric for heat-exchanger / radiator parts.
Built the FinDensity iOS app for MAHLE. Developed a custom AVCapture camera screen with focus-square framing and image cropping, an Objective-C ImageProcessor (OpenCV-backed) that runs pixel-level analysis to detect and count fins on the captured surface, and a ScanResult screen that reports fins count, total distance and fins density, with part-number tagging and history saved via an FMDB SQLite database.
Engineered raw-RGBA pixel analysis (processUsingPixels) that walks the captured image rows to locate and count fin edges, exposes the result through an ImageProcessor delegate, and converts between camera-cropped pixel dimensions and on-screen distance to compute fins per unit length. Persisted each scan with part metadata and timestamps for the 'Previous Scans' list via FMDB.
Architecture summary: Engineered raw-RGBA pixel analysis (processUsingPixels) that walks the captured image rows to locate and count fin edges, exposes the result through an ImageProcessor delegate, and converts between camera-cropped pixel dimensions and on-screen distance to compute fins per unit length. Persisted each scan with part metadata and timestamps for the 'Previous Scans' list via FMDB.
Delivered through structured phases - discovery, design, build, integration, and launch - with iterative releases and ongoing enhancements across a mobile & industrial inspection delivery.
Manual, disconnected workflows and limited visibility across operations.
An integrated, automated mobile & industrial inspection solution with a unified experience, stronger controls and measurable efficiency.
Outcomes available on request.
Project delivered by the Dogra Technologies engineering team.