Scene Recognition TRIZ Cases

Automated drone systems replace manual binocular observation to improve identification accuracy while expanding coverage area.

A vehicle surroundings monitoring apparatus extracts object image areas and determines types using width ratios and leg positions.

Automated video summarization detects foreground objects and tracks moving elements to generate concise visual summaries for operators.

Segmenting LIDAR processing retains low points for neighbor comparison to detect obstacles missed by ground plane removal.

A vehicle surroundings analysis system fuses multi-sensor data to determine object presence through majority voting logic.

Generative artificial intelligence simulates vehicle trajectories using latent variables to resolve accuracy complexity trade-offs in autonomous navigation.

Multi-source remote sensing data non-parametric tree height model estimates spatially continuous forest heights.

Pattern recognition algorithms process driver behavior and vehicle conditions to anticipate hazards, providing extended warning time before incidents occur.

A lidar point cloud processing method filters first-time detected obstacles using grid confidence degrees to reduce false detection impacts.

A dual exposure recognition device captures vehicle images and light source luminance to identify preceding brake lamps.

Dual models separate familiar from unfamiliar objects, allowing real-time boundary updates to resolve accuracy-versus-adaptability contradictions.

A driving assistance method uses region-specific candidate bounding boxes to enhance object detection accuracy.

Assigns higher computational weights to predicted objects within safety-relevant areas around a vehicle.

Clustering algorithms compute cost functions relative to centroids to reduce false positives in object detection systems.

Semi-supervised marking fuses hyperspectral features via t-SNE dimensionality reduction and local density clustering to automate pixel-level annotation.

Transformed semantic point clouds resolve bounding box limitations by revealing object dimensions, pose, and contour for accurate path planning.

Segmented classifiers generate intermediate probabilities from radar data, enhancing measurement precision and robustness against sensor noise.

Flat 2D templates resolve the contradiction between detection accuracy and high memory requirements in vehicle object detection.

Imitation learning compresses a large teacher model into a lightweight student architecture, enabling real-time object detection on embedded systems.

A parameterized calculation engine corrects position data from vehicle cameras using sensor reference points to estimate distances.

A learning device integrates multiple teacher neural networks to generate refined pseudo labels for student models.

Determines overall integrity from multiple bases to resolve sensor deficiencies and enhance surroundings detection reliability.

A driver assistance system analyzes image data to detect potential threats and estimate safe zones using embedded processors.

Blind source separation reconstructs sparse sensor data using parallel tensor slice pipelines.

Segmented cameras feed data to a controller that activates threat level indicators on a widget, resolving blind spot visibility gaps.

A radar processing system associates multibounce reflections with correct objects using iterative calibration and known location data.

Shape-based relevance detection resolves the contradiction between recognition accuracy and determination capability in dynamic environments.

A vehicle obstacle detection system combines ultrasonic sensors with a camera to acquire images for comprehensive coverage.

A pedestrian detection device groups image blocks and divides them horizontally to identify person candidates based on shape and luminance.

Dual detectors compare reference and target results to correct sheltered object recognition, reducing mis-detection in intelligent driving.

A multi-stage screening process filters satellite image chips using intensity, shape, and template features to isolate candidate detections.

A temporal graph convolutional neural network groups sub-regions by environmental similarity to enhance forecasting accuracy.

A computing system identifies vehicle brands by accumulating recognized text from video frames or detecting associated logos.

A detection module merges overlapping targets using score comparisons to identify objects from moving platforms.

A vehicle control system derives a cutting-in probability index to identify surrounding vehicles and adjusts deceleration magnitude accordingly.

Imaging devices feed an object detection system that identifies collision risks to execute avoidance maneuvers without pilot intervention.

Determines a specific analysis region in camera images for lane detection to eliminate unnecessary computation and accelerate processing.

Main control unit coordinates multiple sensors to determine object detectability status, preventing driver confusion from occluded objects.

Driving controller adjusts vehicle start timing based on recognizer-detected surrounding environment conditions.

Computes directional contours for sensor field of view constraints using logical operations on constituent boundaries.

Statistical analysis of image data determines threshold values for automated forest cover classification.

Parking position setter determines angle parking type via marking line geometry and sets internal coordinates to prevent vehicle protrusion.

Feature extraction model generates difference information from unlabeled image pairs using reconstruction and adversarial loss functions.

An enhanced VoxNet 3D model stabilizes neural network performance through specialized hidden units and pooling layers.

Stationary sensors provide high-confidence object labels to retrain vehicle neural networks for improved detection accuracy.

Multi-channel LIDAR matrices feed neural networks to resolve segmentation errors between close objects, improving tracking accuracy.

A vehicle radar adjusts detection point density based on distance to reduce processing load.

A target object detection system uses adaptive weak classifier updates to enhance image recognition accuracy in real-time operations.