Truck Tire Image Annotation: Automotive Data Annotation Services India
Shailesh Maurya is a Full-Stack Software Developer specializing in React, Node.js, and scalable backend solutions. He builds high-performance web applications with a strong focus on user experience, reliability, and clean architecture. He also has experience in RAG development, working with LLMs such as Ollama and ChatGPT, along with vector databases like PGVector to build intelligent AI-powered solutions.
The automotive industry is rapidly adopting Artificial Intelligence (AI) for vehicle damage detection, insurance claim automation, autonomous driving, Advanced Driver Assistance Systems (ADAS), and vehicle inspection.
But there is one major challenge: AI cannot accurately identify vehicle damage or road objects without high-quality annotated training data.
A single vehicle image may contain scratches, dents, broken headlights, damaged bumpers, or multiple overlapping defects. Without precise annotations, AI models may struggle to distinguish between damage types and vehicle components.
This is where Srishta Technology, an automotive data annotation company in India, can help.
How Srishta Technology Supports Automotive AI
With 12 years of technology experience and a team of 50 professionals, Srishta Technology provides automotive image annotation and vehicle data labeling services for AI and computer vision applications.
Our expertise includes vehicle damage annotation, car scratch and dent labeling, bounding box annotation, polygon annotation, semantic segmentation, vehicle parts identification, tyre damage detection, object tracking, and ADAS data annotation.
We help automotive companies, insurance technology providers, AI startups, and vehicle inspection platforms transform raw images and videos into structured, AI-ready datasets.
What this image illustrates
The tire sidewall is divided into separate target regions. The orange outline marks the overall tire boundary; cyan
outlines mark tire size/rim-related information; green outlines mark brand/model areas; and magenta marks other
information, according to the image legend
The Truck Tire Image Annotation project involved manually annotating truck tire images and identifying the relevant information available on each tire. The primary focus was on creating accurate and tightly positioned polygon annotations around the required information while following the client's labeling requirements. The project required patience, attention to detail, time management, and continuous coordination with the client.
| Role: Data Annotator | Annotation Tool Labelbox |
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The project was completed using Labelbox, with polygon-based annotation used to accurately mark the required areas on truck tire images. This specialized tooling allowed for precision drawing and attribute tagging required by the complex labeling schema. |
Annotation Workflow
1. Requirement Clarification
Before starting the annotation work, the annotation requirements and expectations needed to be clearly understood. Since the client's requirements were not always fully confirmed at the beginning, clarification was important to avoid unnecessary rework.
2. Initial Sample Annotation
Instead of completing the entire dataset immediately, the first 15–20 images were annotated and shared with the client for review. This allowed the client to confirm whether the correct information was being labeled, boundaries were accurate, the approach matched expectations, and if changes were required. This early validation helped establish the correct approach.
3. Information Identification
Each truck tire image was carefully inspected to identify the information available.
4. Polygon Annotation
Polygon annotation was used to create tight boundaries around the required information. The goal was to keep the annotation as close as possible to the actual target area rather than creating unnecessarily large regions. When the same type of information appeared multiple times, the available duplicate functionality could be used to reduce repetitive work while maintaining accuracy.
5. Quality Review
Every completed image was carefully reviewed before submission. Special attention was given to: correct label selection, tight polygon boundaries, missing information, incorrect/unnecessary annotations, text accuracy, and overall consistency. The focus on tight and accurate labeling resulted in the annotated images being approved.
Key Features / Annotation Work
| Key Feature | Annotation Work |
| Tire Information Identification | Identifying all relevant information visible on the truck tire |
| Label Matching | Matching identified information with the correct client-provided label |
| Text Annotation | Entering the text when the information was clearly visible |
| Zone Marking | Marking the relevant area when the information was identifiable but the text was not visible. |
| Precise Polygon Annotation | Creating tight polygon boundaries around the required information. |
| Manual Annotation | Performing the annotation manually without general copy-paste support |
| Duplicate Handling | Using duplicate functionality when the same annotation was required multiple times. |
| Quality Verification | Reviewing every image to maintain accuracy and consistency. |
Major Project Challenges
1. Unclear Requirements
One of the major challenges was that some requirements were not completely clear or finalized by the client at the beginning of the project. Because of changes or clarifications received later, previously completed work sometimes had to be updated again. In some situations, 100+ images required rework, which consumed additional time and effort.
2. Image Expiry / Availability
Another important challenge was the limited availability of images on the Labelbox portal. After the client uploaded the images, a timer started, and the images remained available on the portal for a fixed period. Therefore, knowing the exact image expiry deadline was important for planning workload priorities effectively.
3. Time Management
Since the annotation process was largely manual, each image required careful inspection and accurate polygon placement. Balancing accuracy, rework, client feedback, image expiry, and the project deadline was an important and continuous part of the project.
4. Client Coordination
Effective communication with the client was an important part of the project. For large annotation projects, it is beneficial to confirm the following before starting the complete dataset: final annotation requirements, labels and labeling rules, special cases and exceptions, expected output format, project delivery deadline,
image upload and expiry timeline, and the required review process. The 15–20 image validation approach was particularly useful because it allowed the client to identify changes at an early stage rather than after hundreds of images had been completed.
5.Quality & Accuracy
Accuracy was one of the primary priorities throughout the project. The polygon boundaries were kept as tight as possible around the required information, and each image was reviewed before submission. The combination of careful manual annotation, tight polygon placement, requirement validation, and continuous review helped maintain consistent annotation quality.
Results
The project demonstrated the ability to:
- Annotate truck tire images accurately using polygon-based annotation.Identify and label relevant tire information.
- Handle text and non-visible information according to requirements.
- Maintain tight annotation boundaries.
- Manage large-scale manual annotation work.
- Adapt to client feedback, perform rework when requirements changed, and work under imageavailability deadlines.
- Deliver consistently reviewed and approved annotations.
Key Learning & Conclusion
This project highlighted that data annotation is a highly detail-oriented and time-consuming
process. Accurate annotation requires patience, concentration, consistency, and proper time
management. It also demonstrated the importance of clear communication between the client and
annotator before starting large-scale work. For future projects, confirming requirements, labels,
review criteria, delivery deadlines, and image-expiry timelines at the beginning can significantly
reduce unnecessary rework.
Overall, successful annotation is not only about marking images accurately; it also depends on clear requirements, early validation, effective communication, careful time management, and consistent quality control.
Why Choose Srishta Technology for Vehicle Data Annotation?
Srishta Technology combines automotive annotation experience, scalable resources, custom annotation guidelines, and human-in-the-loop quality assurance.
Whether your project involves vehicle damage detection, tyre defect annotation, automotive image segmentation, autonomous driving, or ADAS training datasets, our team can support your requirements with customized labeling workflows.
We also offer pilot annotation projects, allowing clients to evaluate annotation quality before scaling to larger datasets.
Frequently Asked Questions
What is automotive data annotation?
Automotive data annotation is the process of labeling vehicle images, videos, and sensor data to train AI models for damage detection, object recognition, vehicle inspection, and autonomous driving applications.
Does Srishta Technology provide vehicle damage annotation?
Yes. Srishta Technology supports vehicle damage annotation, including scratches, dents, cracks, damaged components, bounding boxes, segmentation, and custom damage classification.
Can Srishta Technology support automotive AI datasets?
Yes. Srishta Technology can support automotive computer vision projects through customized annotation, structured dataset delivery, and human quality assurance.
Build Better Automotive AI with Srishta Technology
Accurate automotive AI starts with accurately annotated data.
Srishta Technology Private Limited helps automotive and AI companies transform raw vehicle images into structured, high-quality training datasets.
Looking for a reliable automotive data annotation company in India?
+91 93543 34258 
+91 93543 34258