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In the rapidly advancing landscape of modern technology, image analysis has emerged as a crucial tool across various applications. High-precision image analysis technology demonstrates its indispensable value in areas such as facial recognition, clothing identification, vehicle detection, and night vision analysis. This article introduces the exceptional accuracy of AIMS in image analysis through eight carefully selected videos, showcasing its application and challenges in different scenarios and conditions.
The core module of the AIMS system is the AI Flow (pipeline), which primarily provides AI image analysis capabilities. Key features of the AI Flow include:
- Support for flexible AI Flow (pipeline) configurations, allowing adjustments based on specific needs to accomplish various tasks.
- AI Flow supports over 40 different DL and CV algorithms, catering to a wide range of tasks.
- User-friendly AI Flow design tools, featuring No-Code functionality, enabling personnel without algorithm knowledge to use the system easily.
- Comprehensive Object Indexing capabilities that significantly enhance object search performance.
- Integration of highly automated MLOps processes, allowing models to continuously self-learn and automatically improve in accuracy and stability.
By leveraging these advanced features, AIMS delivers high-precision image analysis, ensuring its effectiveness and reliability in diverse application scenarios.
Vehicle
- Traffic management: Monitor traffic flow, detect violations.
- Parking management: Identify occupied parking spaces.
Motorcycle
- Traffic enforcement: Monitor violations like running red lights or wrong-way driving.
- Safety monitoring: Identify riders without helmets.
- Parking management: Monitor motorcycle parking areas.
People
- Crowd monitoring: Count pedestrian flow, optimize crowd management.
- Safety monitoring: Detect suspicious behavior, enhance public safety.
Head
- Headcount: Count the number of people in specific areas.
- Safety monitoring: Detect workers without safety helmets, ensure construction site safety.
Time Periods
- Morning: Increasing light, but shadows and glare can affect object recognition.
- Afternoon: Sufficient light, but strong light and long shadows can interfere with analysis.
- Evening: Decreasing light, handling insufficient light and increasing shadows.
- Night: Low light, relying on artificial lighting and infrared cameras, challenges include noise and low dynamic range.
Weather Conditions
- Raining: Rain may blur the lens, increased reflections, reduced image clarity.
- Snowing: Snowflakes obscure vision, increased background brightness, difficulty distinguishing objects.
Seasonal Conditions
- Summer: High temperatures may affect camera performance, strong light and heat waves interfere with image stability.
- Winter: Low temperatures may cause equipment failure, snow and frost affect visual clarity.
Image analysis under these conditions requires coping with various environmental changes and lighting conditions to ensure accuracy and stability. AIMS uses special image preprocessing methods to enable deep learning to effectively detect objects.
Face Detection
- In-store hallway
- In-store escalator
Face Classification
- Mask detection / Storefront
- Gender detection / Storefront
- Age detection / Storefront
Face Recognition
- Storefront
- Face movement reduces detection rate.
- Fast-moving faces reduce detection rate.
- Small faces reduce recognition accuracy.
- Face clarity affects recognition accuracy.
- Backlighting reduces recognition accuracy.
AIMS's facial detection and recognition functions consider these challenges in software design and deep learning models, ensuring high and stable accuracy.
Detection:
- Indoor
- Indoor / Fisheye lens
- Street / Daytime
- Night Vision / Raining
Classification:
- Gender / Daytime street
- Gender / Night Vision
- Color of upper clothes / Plaza
- Upper clothes category / Street
- Color of lower clothes / Street
- Lower clothes category / Street
Recognition:
- Sidewalk / Winter
- Street / Summer
Detection
- Indoor: Lighting changes and crowded environments.
- Indoor / Fisheye lens: Lens distortion effects.
- Street / Daytime: Strong light and shadow changes.
- Night Vision / Raining: Low light and rain interference.
Classification
- Gender / Daytime street: Diverse clothing and background interference.
- Gender / Night Vision: Insufficient light.
- Color of upper clothes / Plaza: Distinguishing similar colors.
- Upper clothes category / Street: Variety of upper clothing styles.
- Color of lower clothes / Street: Color changes and background interference.
- Lower clothes category / Street: Variety of lower clothing styles.
Recognition
- Sidewalk / Winter: Bulky clothing and snow scene interference.
- Street / Summer: Clothing diversity and strong light.
Detection
- Low angle / Daytime
- High angle / Night
Classification
- Vehicle category / Daytime
- Vehicle category / Snow
- Vehicle category / Night
- Vehicle color / High angle
- Vehicle color / Low angle
Recognition
- High angle
- Low angle
Light Changes
- Daytime and nighttime light differences, especially in low-light environments.
Angle
- High and low angle shooting may cause perspective distortion, affecting recognition accuracy.
Weather Conditions
- Rain and snow weather conditions, such as raindrops or snowflakes blurring the lens.
Vehicle Occlusion
- Vehicles blocking each other in traffic congestion or parking lots.
Vehicle Color and Type
- Accuracy in classification and recognition of different vehicle colors and types.
Dynamic Environment
- High-speed moving vehicles increase detection and recognition difficulty.
Detection
- Light rain / B/W
- Heavy rain / B/W
- Crowded / B/W
- Backlighting / B/W
- Low angle / Large objects / Full color
- High angle / Small objects / Full color
- Snowing / Low angle / Large objects / Full color
Classification
- Gender / Full color
- Color of clothes / Full color
- Clothes category / Full color
- Vehicle category / Light rain / B/W
- Vehicle category / Heavy rain / B/W
- Vehicle category / Low angle / Full color
- Vehicle category / High angle / Full color
- Color of vehicle / Low angle / Full color
- Color of vehicle / High angle / Full color
Recognition
- Low angle / Large objects / Full color
- Snowing / Low angle / Large objects / Full color
- Insufficient or changing light
- Weather conditions like rain and snow affecting image clarity
- Backlighting and shadow interference
- Perspective distortion leading to recognition difficulties
- Accurate identification of high-speed moving objects
- Increased complexity in classifying diverse clothing and object types
Detection
- Low angle / Daytime
- Low angle / Daytime
- High angle / Night
Classification
- Vehicle category / Daytime
- Vehicle category / Snow
- Vehicle category / Night
- Vehicle color / High angle
- Vehicle color / Low angle
Recognition
- High angle
- Low angle
- Light changes
- Perspective distortion
- Weather effects
- Vehicle occlusion
- Vehicle color and type differentiation
- High-speed moving objects in dynamic environments
- Frontal and side face
- With glasses
- Without glasses
- With mask
- Without mask
- Accuracy under different lighting conditions
- Various facial obstructions (e.g., glasses and masks)
- Face angle variations
- Recognizing moving faces
- Accurate recognition in crowded scenes
When performing clothing recognition, conditions, shooting angles, object sizes, and traffic flow significantly impact the results. Here are the applications and their importance in different scenes:
| Scene | Condition | Shooting Angle | Object Size | Traffic Flow |
|---|---|---|---|---|
| 1 | Winter | High | Medium | Medium |
| 2 | Autumn | High | Small | Small |
| 3 | Summer | Low | Large | Small |
| 4 | Cloudy | Low | Medium | Small |
| 5 | Cloudy | Low | Large | Medium |
| 6 | Sunny | Low | Large | Small |
| 7 | Winter | Low | Medium | Medium |
| 8 | Winter | High | Large | Large |
| 9 | Cloudy | Low | Large | Large |
| 10 | Cloudy | Low | Small | Small |
| 11 | Summer | Low | Large | Large |
| 12 | Summer | Low | Large | Large |
- Light Changes: Different lighting conditions affect image clarity and recognition accuracy.
- Weather Conditions: Adverse weather such as rain and snow can interfere with camera visibility.
- Shooting Angle: High and low angles can cause object distortion, making recognition difficult.
- Moving Objects: Fast-moving objects can blur, increasing recognition difficulty.
- Complexity of Crowded Scenes: Distinguishing individuals in crowded scenes is more challenging.
- Backlighting and Shadows: Backlighting and shadows can obscure important details, affecting recognition.