Road Surface Corner Case Image Dataset

Description: This dataset focuses on rare but high-risk road anomalies—known as “corner cases”—that can occur in autonomous driving and intelligent perception systems. It includes high-resolution synthetic images depicting scenarios such as fallen traffic cones, pedestrians lying on the ground, overturned bicycles, and scattered road obstacles (e.g., boxes, fabric strips). The emphasis is on the rarity and danger of the targets, aiming to enhance a model’s ability to detect low-frequency yet high-severity events with strong generalization and robustness.

The dataset is designed for safety perception testing, extreme case robustness evaluation, anomaly detection, and driver assistance model training. It is suitable for use in both autonomous driving validation pipelines and advanced driver assistance systems (ADAS), enabling improved detection performance in critical safety scenarios.

All images are captured at 1920×1080 resolution in realistic road environments using simulated front-facing in-vehicle cameras or dashcams. Data covers diverse driving contexts such as urban streets, industrial park roads, and parking lots, with natural variation in lighting, shadows, occlusions, and background complexity. The set includes multiple variations of fallen object states and environmental conditions to support comprehensive training coverage.

Annotations are provided in JSON, COCO, or VOC formats, with bounding box detection and class labels. Categories include fallen human, fallen traffic cone, abnormally parked bicycle, and miscellaneous scattered obstacles. Balanced representation across object types and scene conditions ensures robust model training for rare event detection.

By combining photorealistic rendering, diverse scene composition, and detailed human annotation, this dataset provides an essential resource for developing perception models capable of handling extreme and rare road scenarios, improving safety and reliability in real-world driving.

Keywords: corner case fallen cone road anomaly safety detection ADAS rare event detection autonomous driving

Sector: Intelligent Transportation / Autonomous Driving / Road Safety Systems / Computer Vision

Metrics: Rare object detection recall, anomaly classification precision

Entities: Fallen traffic cones, fallen pedestrians, overturned bicycles, scattered road obstacles

Price Range (USD): 4000–20000

Source type: Synthetic data generation from simulation environment, human annotation

Sample Images

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