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Vehicle detection using YOLO in Keras runs at 21FPS
| Date | Stars |
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| 2026-07-24 | 392 |
| 2026-07-25 | 392 |
| 2026-07-28 | 392 |
| 2026-07-30 | 392 |
| 2026-08-06 | 392 |
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# Vehicle Detection Project
This is a project for Udacity self-driving car Nanodegree program. The aim of this project is to detect the vehicles in a dash camera video. The implementation of the project is in the file vehicle_detection.ipynb. This implementation is able to achieve 21FPS without batching processing. The final video output is [here](https://www.youtube.com/watch?v=PncSIx8AHTs).
In this README, each step in the pipeline will be explained in details.
## Introduction to object detection
Detecting vehicles in a video stream is an object detection problem. An object detection problem can be approached as either a classification problem or a regression problem. As a classification problem, the image are divided into small patches, each of which will be run through a classifier to determine whether there are objects in the patch. Then the bounding boxes will be assigned to locate around patches that are classified with high probability of present of an object. In the regression approach, the whole image will be run through a convolutional neural network to directly generate one or more bounding boxes for objects in the images.
| classification | regression |
|----------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------|
| Classification on portions of the image to determine objects, generate bounding boxes for regions that have positive classification results. | Regression on the whole image to generate bounding boxes |
| 1. sliding window + HOG 2. sliding window + CNN 3. region proposals + CNN | generate bounding box coordinates directly from CNN |
| RCNN, Fast-RCNN, Faster-RCNN | SSD, YOLO |
In this project, we will use tiny-YOLO v1, since it's easy to implement and are reasonably fast.
## The tiny-YOLO v1
### Architecture of the convolutional neural network
The tiny YOLO v1 is consist of 9 convolution layers and 3 full connected layers. Each convolution layer consists of convolution, leaky relu and max pooling operations. The first 9 convolution layers can be understood as the feature extractor, whereas the last three full connected layers can be understood as the "regression head" that predicts the bounding boxes.
There are a total of 45,089,374 parameters in the model and the detail of the architecture is in list in this table
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
convolution2d_1 (Convolution2D) (None, 16, 448, 448) 448 convolution2d_input_1[0][0]
____________________________________________________________________________________________________
leakyrelu_1 (LeakyReLU) (None, 16, 448, 448) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 16, 224, 224) 0 leakyrelu_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 32, 224, 224) 4640 maxpooling2d_1[0][0]
_______________________________________________________________Excerpt of 11,673 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7bb35344cf415438, topic:yolo, readme:object detection