GP_Slides_V3 .pptx
- 2. Traffic Accident: Detection Under supervision of Dr. Amani Hassan Eng. Dina Amr Presented by Mohanad Talat 20190561 Eslam Mohamed 20190098 Maher Esmat 20190410 Sayed Shaaban 20190254 Ahmed Mohamed 20190071 Khalid Hassan 20190187
- 3. INTRODUCTION RELATED WORK MODEL CONCLUSION DATASET PLAN FRAMEWORK RESULTS TABLE OF CONTENT
- 4. INTRODUCTION
- 5. INTRODUCTION ● According to the World Health Organization, Egypt has one of the highest rates of road accidents worldwide. In 2016, WHO estimated road fatalities in Egypt at 9,287. The latest WHO data published in 2020 shows that road traffic accidents deaths in Egypt reached 10,141. ● Fast response to an accident is so crucial that a 7 to 12 minutes delay, can result in the odds of death increasing by 46 percent ● Traffic accidents pose a significant threat to public safety and cause numerous injuries and fatalities worldwide. Prompt detection of accidents can enable swift emergency responses, minimizing the potential impact and saving lives. This problem definition focuses on developing a model to detect traffic accidents using popular object detection models such as YOLO, SSD, and Faster R-CNN. The goal is to compare these models and select the most effective one for deployment in CCTV cameras.
- 6. Problem Definition The Problem is to build a reliable and accurate traffic accident detection system using state-of-the-art object detection models. The system should be able to analyze live video feeds from CCTV cameras and determine whether an accident has occurred or not, based on the presence of specific visual cues such as collision, and vehicle damage. The system's performance will be evaluated based on accuracy, precision, recall, and computational efficiency.
- 7. Objective ● The aim of this project is to help reduce traffic accidents. ● By trying to predict and categorize the accident. ● We don’t need to depend on human. ● But how could this possibly be useful ??
- 8. Applications ● Notify authorities as soon as an accident occurs. ● By using CCTV cameras installed in the accident location . ● So we avoid to depend on the human.
- 9. RELATED WORK
- 10. Object Detection Object detection is the field of computer vision that deals with the localization and classification of objects contained in an image or video. Drawing bounding boxes around detected objects which allow us to locate them in a given scene. Object Detection
- 11. Semantic Segmentation The process of dividing a digital image into multiple image segments, also known as image regions or image objects, is known as image segmentation (sets of pixels). Image segmentation is commonly used to find objects and boundaries (lines, curves, and so on) in images. Semantic Segmentation
- 12. Pedestrian Detection predicts information about the pedestrian's position, provides a comprehensive overview and arguments for replacing based on the detection in the current frame. It has an obvious extension to automotive applications due to the potential for improving safety systems. Pedestrian Detection
- 13. An Attention-guided Multi-stream Feature Fusion Network for Localization of Risky Objects in Driving Videos Link
- 14. PLAN
- 15. PLAN
- 16. FRAMEWORK
- 17. • Python in implementation FRAMEWORK - CADP dataset Annotation For Traffic CCTV Videos Programming Language DataSet
- 18. DATASET
- 19. DATASET - The dataset consists of 1,416 video segments collected from YouTube - The dataset contains frames of different videos of CCTV traffic cameras. - Average length of videos in our dataset is 366 frames per video with longest video consisting of 554 frames. - The dataset has both low and high resolution. - Total duration of videos is 5.2 hours.
- 21. Normalize image size : - We are doing this step because the CNN models should take the same input size. - The size of image is (500,500,3) - (Width,height, RGB) Preprocessing Original Normalized(500,500,3)
- 22. Segmentation and morphology : - As we can see we are going to use this step in our preprocessing because it remove the most important feature (cars) from images. Preprocessing
- 23. Duplicate data has been deleted Bad quality data has been deleted All frames that do not guarantee a traffic accident have been deleted Data filtering Not interested
- 25. MODEL
- 27. ● The image is fed to CNN which generates a convolutional feature map ● First, Feed feature map into an independent fully convolutional network Region Proposal Network(RPN) ● Second feed generated proposals to a Fast R- CNN network. ● Faster R-CNN is better because ROI generation is now integrated within network ● One drawback of Faster R-CNN is that the RPN can be slow, it is also unable to detect small objects Faster R-CNN (ResNet-50)
- 28. YOLO_V5_ Overview Evolution from previous versions Key features and improvements Comparison to other object detection algorithms (Faster R-CNN, SSD, etc.) D. Supported frameworks and languages
- 29. YOLO_v5 Architecture A. High-level architecture B. Backbone network (e.g., CSPDarknet53, EfficientNet) C. Neck and head components D. Loss functions and training process
- 30. YOLO_V5 • You Only Look Once • Proposes using an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once. It differs from the approach taken by previous object detection algorithms, which repurposed classifiers to perform detection • YOLO is a single-stage model so it can process images much faster than RCNN family and require much less training data, However it is not as accurate as RCNN family and cannot identify overlapping objects
- 31. SSD SSD_mobilenet _v2_320*320 SSD_mobilen et_v2_fbnlite_ 640*640 SSD_mobilenet_ v2_fbnlite_320*3 20
- 32. SSD_mobilenet (v2_320*320) - Balanced Accuracy and Efficiency: The model offers a good balance between detection accuracy and computational efficiency. It is suitable for real-time applications on resource-constrained devices, where both accuracy and efficiency are important. - Multi-Scale Feature Extraction: The MobileNetV2 backbone enables the extraction of multi-scale features, allowing the model to detect objects at different scales and adapt to varying object sizes present in the input images.
- 33. MobileNetV2 Architecture Introduction to MobileNetV2 Key features and design principles Lightweight and efficient architecture Depthwise separable convolutions Inverted residual blocks
- 34. SSD_mobilenet (v2_fbnlite_640*640) - Improved Efficiency: The Fused Batch Normalization Lite (FBNLite) variant of MobileNetV2 reduces memory consumption and improves inference speed. It is optimized for efficient object detection on mobile and embedded devices, making it ideal for applications where computational resources are limited. - Aspect Ratio Flexibility: The elongated aspect ratio (640x640) allows the model to handle objects with non-standard aspect ratios more effectively. This can be beneficial when dealing with objects that are elongated or have specific aspect ratio characteristics.
- 35. SSD_mobilenet (v2_fbnlite_320x320) - Enhanced Efficiency: The FBNLite variant of MobileNetV2 further improves computational efficiency compared to the standard MobileNetV2 backbone. It reduces memory usage and speeds up inference, making it well-suited for real-time applications. - Accurate Object Detection: While sacrificing some accuracy compared to higher-resolution models, SSD MobileNetV2 FBNLite 320x320 still maintains good object detection performance. It strikes a balance between accuracy and efficiency, making it suitable for applications where real-time performance is critical
- 36. RESULTS
- 39. CONCLUSION
- 40. Conclusion . Finding data . Applying all the required preprocessing . Find models to work on the data . Apply different models on the data . Compare between the models . Use the best model on real time camera . Deployment the model on a Website Interface