Enhancing Traffic Monitoring in Dhaka City through Object Detection

Table of Contents

1. Introduction
2. Object Detection
3. Object Detection for Traffic Monitoring
4. Challenges in Dhaka City
5. Dataset Description
6. Data Preprocessing
7. Model Selection and Training
8. Augmentations and Improving Accuracy
9. Results and Performance
10. Conclusion
11. Future Work

Introduction

Object detection is an important aspect of computer vision that involves identifying and localizing objects in images or videos. In this article, we will explore the concept of object detection and its applications, particularly in the context of traffic monitoring. We will discuss the challenges faced in the city of Dhaka and the dataset used in our project. We will also delve into the data preprocessing steps, model selection, and training process. Additionally, we will explore the use of augmentations to improve accuracy and Present the results and performance of our model. Finally, we will conclude with future prospects and areas of further research in this field.

Object Detection

Object detection refers to the task of identifying and localizing objects in images or videos. It involves two main steps: identifying the presence of objects and determining their precise locations within the image. Object detection has numerous applications in various fields, including surveillance, autonomous vehicles, and medical imaging. By accurately detecting objects, we can Gather valuable insights, automate processes, and enhance decision-making capabilities.

Object Detection for Traffic Monitoring

One area where object detection plays a crucial role is traffic monitoring. In cities like Dhaka, where traffic congestion is a significant issue, implementing a successful object detection method can have a widespread impact. By accurately detecting and tracking vehicles, we can develop a full-time traffic monitoring system. This system can analyze traffic Patterns, automate traffic flow control, and even detect stolen vehicles. Additionally, object detection can enable smart control over roadside signals and contribute to automating driving processes.

Challenges in Dhaka City

Dhaka city faces several challenges when it comes to traffic management. One significant issue is the class imbalance problem. The city primarily consists of motorbikes and cars, with a limited number of pickups and buses. This imbalance creates difficulties in accurately detecting and monitoring different types of vehicles. To address this issue, we need to explore methods that improve the detection accuracy for underrepresented classes.

Dataset Description

In our project, we utilized a dataset specifically designed for the Dhaka AI competition. The dataset consists of 21 classes of vehicles, including cars, buses, and motorcycles, among others. The training set comprises approximately 3,003 annotated images, while the test set initially contained 500 unannotated images, which were later annotated for better results. The dataset poses a challenge due to the class imbalance problem, particularly in the case of minor classes.

Data Preprocessing

To prepare the dataset for model training, we performed various preprocessing steps. Firstly, we removed duplicate and empty annotations to ensure data quality. Next, we resized the images to a standardized resolution to optimize training time and efficiency. We also applied different augmentations, such as rotation, translation, scaling, and random changes in hue, saturation, and brightness. Additionally, we addressed the class imbalance problem by applying specific augmentations to the underrepresented minor classes.

Model Selection and Training

In our project, we explored different models for object detection, including YOLO (You Only Look Once) versions and EfficientDet. We initially chose YOLOv5 for its faster performance, lower GPU memory requirement, and better results compared to other EfficientDet models. The YOLOv5x model, which consists of 335 layers and approximately 47 million parameters, showed promising performance in our experiments.

Augmentations and Improving Accuracy

To further improve accuracy, we incorporated various augmentations into the training process. In addition to the previously Mentioned augmentations, we also applied darkening and blurring to generalize the model's performance in low-light and foggy conditions. These augmentations played a crucial role in reducing the class imbalance problem and enhancing the overall accuracy of our model.

Results and Performance

After training our models and conducting inference on the test dataset, we evaluated the performance of our approach. The ensemble of the two best-trained models achieved an accuracy of 34.56% on the test data. We compared our results with state-of-the-art models and observed that our approach showed competitive performance. However, we acknowledged the need for further improvements to match the highest-ranked models.

Conclusion

In conclusion, our project focused on object detection for traffic monitoring in Dhaka city. We explored the challenges posed by the class imbalance problem and proposed solutions through data preprocessing and augmentations. By training and evaluating different models, we achieved promising accuracy results. However, there is still room for improvement, and future work should involve exploring more datasets, fine-tuning hyperparameters, and implementing advanced techniques such as focal loss. Through these efforts, we aim to enhance the accuracy and practicality of our model for real-world deployment.

Future Work

In future endeavors, we aim to further improve our object detection model. This includes exploring genetic algorithms for hyperparameter evolution, which can optimize our model's performance. We also intend to investigate other state-of-the-art models, such as Faster R-CNN and Mask R-CNN, to compare their effectiveness with our current approach. Furthermore, we aspire to convert our model into a real-time system, enabling it to process video feeds for live traffic monitoring. By continually refining our methods, we hope to achieve even better results and contribute to the field of object detection.

Highlights

• Object detection plays a crucial role in various applications, including traffic monitoring.
• Dhaka city faces challenges due to the class imbalance problem in vehicle detection.
• The dataset used in our project consists of 21 classes of vehicles, with a focus on Dhaka city.
• Data preprocessing, including augmentations, was performed to enhance the accuracy of object detection models.
• Our approach achieved competitive accuracy results, with potential for further improvements.

FAQs

Q: What is object detection? Object detection is a computer vision task that involves identifying and localizing objects in images or videos.

Q: How can object detection be used for traffic monitoring? Object detection enables the development of a full-time traffic monitoring system, providing insights into traffic patterns and facilitating automation of traffic flow control.

Q: What challenges does Dhaka city face in implementing object detection for traffic monitoring? Dhaka city experiences a class imbalance problem, primarily consisting of motorbikes and cars, which poses difficulties in accurately detecting other types of vehicles.

Q: How was the dataset prepared for model training? The dataset underwent preprocessing steps, including removing duplicate and empty annotations, resizing images, and applying augmentations to address the class imbalance problem.

Q: What models were used in the project? We explored models such as YOLOv5 and EfficientDet, with YOLOv5x showing promising performance.

Q: How were accuracy and performance evaluated? The ensemble of the two best-trained models achieved an accuracy of 34.56% on the test data. Performance was assessed based on metrics such as mean average precision and recall.

Q: What are the future plans for the project? Future work involves exploring genetic algorithms for hyperparameter optimization, investigating other state-of-the-art models, and converting the model into a real-time system for video-based traffic monitoring.

Q: What were the major highlights of the project? The project highlighted the significance of object detection in traffic monitoring, the challenges faced in Dhaka city, and the effectiveness of preprocessing and augmentations in improving accuracy. The results achieved were competitive, with potential for further improvements.