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Manufacturing industries are rapidly integrating computer vision to improve quality control, reduce defects, and increase automation. At Jeenish AI Solutions, we support this transformation by delivering precise, high-quality training data tailored to real-world factory conditions. From product inspection to robotic guidance, our human-in-the-loop workflows ensure accuracy and adaptability for industrial AI systems.
2D Bounding Box & 3D Cuboid Annotation for Defect Detection
We annotate machinery components, products on assembly lines, and packaging defects using 2D bounding boxes and 3D cuboids, enabling AI systems to identify anomalies in shape, orientation, or presence.
Example: Drawing bounding boxes around cracked or misaligned engine parts in factory floor images for automated inspection systems.
Semantic Segmentation of Components & Materials
Using semantic segmentation, we label areas pixel by pixel—such as conveyor belts, bolts, circuit boards, or different metal types. This supports robotic vision systems with spatial awareness and object distinction.
Example: Segmenting soldered joints vs. PCB surfaces in high-resolution images for electronic component quality checks.
Object Tracking in Assembly Lines
Our teams track objects frame-by-frame as they move through manufacturing pipelines using video annotation and object tracking, allowing real-time monitoring of assembly progress or robotic alignment.
Example: Tracking bottles moving on a filling line to detect blockages, speed inconsistencies, or missing units.
Activity Recognition in Industrial Safety Monitoring
We annotate human worker activities (e.g., standing, bending, operating machinery) for activity recognition, helping train models to detect unsafe behaviors or ergonomically risky postures.
Example: Detecting if a worker is reaching into a running machine zone, triggering real-time safety alerts.
Dataset Design for Specific Machine Vision Models
We collaborate with industrial clients to build custom datasets based on use case—like surface inspection, part classification, or robotic pick-and-place—by planning edge-case coverage and labeling formats.
Example: Designing a dataset of different screw types on metal panels with fine-grained labels for an automated screwing arm.
Continuous QA & Label Validation for Precision
We apply expert review and cross-validation to ensure labeling precision for machine-critical models. Our QA process filters inconsistent labels, optimizes formats, and supports retraining.
Example: Validating thousands of weld point annotations across weld seams to improve robotic welders’ targeting accuracy.
Why It Matters?
In manufacturing, efficiency and accuracy are everything. With Jeenish AI Solutions, companies gain access to scalable, reliable annotation pipelines that fuel industrial AI systems—from defect detection to predictive maintenance. We bring the precision, domain understanding, and fast turnaround needed to support real-time, factory-grade computer vision solutions.