The document describes a study that used Faster R-CNN, a deep learning approach, to detect road objects like vehicles, pedestrians, and traffic signs in Bangladesh. The researchers collected images to train a neural network to identify 19 object classes. Their model achieved 86.42% accuracy and was able to detect objects in various lighting and traffic conditions, though it struggled when objects were extremely close together. The study aims to help analyze traffic and potentially assist autonomous vehicles in Bangladesh.

1. Road Object Detection in Bangladesh using
Faster RCNN: A Deep learning Approach
Anik Datta, Tamara Islam Meghla, Tania Khatun, Mehedi Hasan
Bhuiya, Shakilur Rahman Shuvo and Md. Mahfujur Rahman
PAPER ID
282

2. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Outline
• Introduction
• Motivation
• Methodology
• Result and Discussion
• Conclusion
ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH • 2

3. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Introduction
• Object detection is a combination of computer vision and image
processing technology that can detect similar objects of a certain
class from images and videos.
• The role of object detection is very important for autonomous car,
intelligent driving assistance and advanced traffic analysis
nowadays.
•In the case of traffic analysis and intelligent driving assistance in
Bangladesh, it is very important to properly identify all the objects
from real time video.
•Our main objective is to accurately identify every object on the road
such as pedestrians, vehicles, traffic lights and traffic signs in a
populous country like Bangladesh and use them to better analyse
the traffic situation or use this information in automated driving.
3ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

4. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Motivation
• Road accident in Bangladesh has become an important issue.
•There has been an alarming rise in road accidents,
significantly highway accidents, in Bangladesh over the past
few years.
•Road accidents are mainly occurred for drivers carelessness
and violating traffic laws.
•To solve such problems we have done this research to save
some lives from accidents and to extract important traffic
information so that the traffic system can be further improved in
the future.
4ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

5. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Methodology
The reason why did we choose to use Faster R-CNN
Faster R-CNN is one of the most popular object detection
algorithm. We chose Faster R-CNN for object detection
because it is faster and it fixed the problem using Region
Proposals Network(RPN).Test time speed of Faster R-CNN for
per image 0.2 seconds.
Although YOLO is faster than Faster r-cnn. YOLO has some
drawbacks in object detection. It is difficult for YOLO in
detecting objects that are small and close to each other. Even
if the aspect ratio of the image changes, YOLO faces difficulty
in object detection.
5ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

6. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Methodology
Data Accumulation
We collect our findable image by clicking photographs and also
collect images for our dataset from google. After collecting the
images after that we label the desired objects in every image.
Faster RCNN
We use Faster R-CNN architecture for object detection. It is a
modified version of Fast R-CNN. At first Faster R-CNN extracts the
input image using convolution neural networks and passes those
maps through the region proposal network(RPN). Faster R-CNN
uses convolution neural network. It is very effective in image
recognition and classification. It can successfully identifying face,
object and traffic sign.
6ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

7. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Methodology
7
Fig. 1: Working procedure of RPN Fig. 2: Object detection with Faster R-CNN
ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

8. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Methodology
Training and Prediction
We have trained our Neural Network
through TensorFlow api by Faster R-CNN
in 19 object classes.
We used inception v2 coco architecture
utilize due to high accuracy among
convolution neural networks. That
inception v2 has the best learning rate for
faster RCNN.
Our neural network has completed
80,651 global steps during training and
has a loss value of 0.0122.
We have enlisted the help of Google
colab to complete the above process.
8ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

9. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Result and Discussion
Performance Analysis
We took a huge number of instances
from the data set to train and
observed them with precise needs.
For testing purposes, we select 199
instances with same label and
125images as different label.
After that, the prediction accuracy
jumped up to 86.42%.
9ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

10. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Result and Discussion
Real-time detection result
• We noticed some promising things while checking our results like we
were able to detect objects with almost equal efficiency in day and night
or in low light.
10
Fig. 3: Detection in day(Bright light) and night(low light)
ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

11. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Result and Discussion
• Our neural network has been able to detect objects efficiently
with moderate and low traffic on the road. And succeeds in
detecting all types of small-to-large-medium objects in all
situation.
11
Fig. 4: Detection in medium and low traffic
ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

12. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Result and Discussion
• Our purposed neural network can detect and analyze objects
well even when the objects are very close to each other and
even during high traffic on the road.
12
Fig. 5: Detection in high traffic
ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

13. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Result and Discussion
• But it only gets into trouble when the objects are too close
together and cling to each other in a crowded state.
•When detecting objects on high traffic or in close proximity to
the road, objects in the front make it difficult to detect the
objects behind because the objects in the front cover the
objects behind and therefore our proposed neural network can
only accurately detect objects in the front.
13ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

14. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Conclusion
• In this paper our proposed solution is able to analyse in different
situations like night, daylight or high traffic, medium traffic, low
traffic fairly efficiently.
• And our proposed solution is able to track multiple objects which
is possible due to its huge object class.
• When we test through this data set, some objects are mistakenly
detected and many times some objects are not detected.
• The reasons we have investigated are that if it is processed on a
low powered machine without a GPU, there is a possibility of
incorrect detection and another reason is that the images we
used to create the data set have different traffic situations.
• It could be further improved in the future to be used more
efficiently in real-time traffic analysis.
14ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH

15. WIECON-ECE 2020
2020 IEEE International Woman in Engineering (WIE)
Conference on Electrical & Computer Engineering
wiecon-ece.org
Thanks
15ANIK DATTA, ROAD OBJECT DETECTION IN BANGLADESH USING FASTER RCNN: A DEEP LEARNING APPROACH