An industrial forestry operations company
Onboard-video computer vision that enforces machinery safety and estimates trailer load levels.
Safer access
Data-driven safety enforcement
Load %
Accurate trailer load estimation
Onboard
Video, offline-ready deployment
Operator safety during machinery access is a critical challenge in forestry. The project identifies unsafe mounting and dismounting practices by analyzing existing onboard video footage, enabling data-driven safety improvements and reducing accident risk.
Managing trailer load limits and understanding machine usage are key operational challenges. The project prevents overloading, estimates log load levels, breaks machine activity into phases, and counts grapple actions using video-based analysis.
We built a pipeline to detect and extract mounting and dismounting events from onboard footage, saving labeled video segments and JSON metadata (date, time, camera, machine) to Azure Storage. Models and source code were delivered via repository with full documentation and sample input and output videos.
We built a second pipeline that analyzes footage to detect trailer overloads and estimate load percentage, generating JSON files with load date, time, and percentage. All outputs, including overload captures, are stored in Azure Storage, with models and documentation delivered via repository.
Improved safety compliance by helping the client identify unsafe machinery-access behaviors and reduce accident risk. The tool supports safety-protocol enforcement and operator training, lowering downtime and incident-related costs.
Preventing overloading and providing accurate load estimates enabled better resource management and planning, reducing safety risks, equipment wear, and downtime, and contributing to cost savings and productivity.
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