Object detection with SSD

June 8, 2019 Petru Radu

Object detection employing Deep Learning

Object detection is a task in computer vision that has been around for very long time, way before the deep learning has emerged as the state of the art in machine vision. In a self-driving car, the camera from the car is acquiring images and it needs to detect different categories of objects in the surrounding environment, such as pedestrians, cars, bicycles, trees and so on.

Neural networks and convolutional neural networks (CNN) in particular are considered a component of the state-of-the-art object detection algorithms. One of the most popular object detection mechanism involving CNN is Single-Shot MultiBox Detector (SSD).

SSD introduction

SSD shares a lot of techniques with another popular algorithm for object detection named YOLO (You Only Look Once). SSD is considered a significant milestone in computer vision because before of this, the task of object detection was quite slow as it required multiple stages of processing. The speed of the object detection algorithms is critical in real time applications like in self-driving cars. The object detection task has been converting from a computer science research problem into a safety requirement in automotive industry.

The original paper that introduced SSD can be found at: http://arxiv.org/abs/1512.02325. From this paper it may be observed that the SSD algorithm outperforms YOLO so is therefore becoming the new state-of-the-art in object detection. In the figure below we may observe how the performance of the object detection task improved over time and CNN boosted its accuracy.

Mean Average Precision of object detection algorithms in time

Algorithm and network architecture in brief

The first stage in the task of object detection is localization. In this stage the objective is to produce a binary classification result on weather any object is present in the image or not. The region where an object has been localized will serve as input for a classification algorithm, which will decide to which category the object belongs. The classification algorithm is considered already available through the training of a state-of-the-art deep neural network architectures employed in image classification.

One of the simplest ideas it may be applied for object detection is called sliding window technique. A window is taken and for each position in the original image the sub-image is passed into a CNN. The CNN will produce a binary result saying if an object is inside that sub-image or not.

If the height and width of the image are denoted with H and W, and the size of the sliding window is denoted by window_size, the following Python pseudo-code may be used for a sliding window object localization task:

for i in range(H):

for j in range(W):

i1 = i * step_size

i2 = i1 + window_size

j1 = j * step_size

j2 = j1 + window_size

w = img[i1:i2, j1:j2]

prediction = cnn.predict(w)

The localization will produce a bounding box, and its dimensions will need to be normalized between (0, …, 1). The problem with this approach is that it has an order of complexity of O(N²), being impractical for real time application. One may observe that a sliding window however, can be implemented as a convolution. Therefore, a typical CNN with some specific architecture may be used to perform object localization.

Such a network architecture is very similar to a regular CNN that has 3-4 convolutional layers followed by 2-3 fully connected layers. However, the particularity of CNN architectures when exploring object localization is replacing the final fully connected layers with convolutional layers of the same size. For example, if a fully connected layer from the original architecture has the shape of 128x1, then the convolutional layer will have the shape of 1x1x128. The figure below illustrates a CNN architecture with the properties discussed here in order to perform object localization:

CNN architecture employed in object localization

By considering the deep learning architecture from the figure above, the input image is considered the sliding window and therefore, the original image region has a slightly larger dimension, a few pixels on width and height. When processing the image region, the center of the region is fixed at the size of the sliding window. Then, 4 shifts are made, so the input to the network architecture from the above image is an image with 4 times more channels.

The extremely interesting observation that was made is that the final 1x1x128 convolutional layers become 2x2x128 layer. The 2x2x128 shape contains 4 vectorized elements that correspond to the 4 input sliding windows. This is the same result as that obtained by doing a 2x2 for loop over the image!

Another issue in object detection is the scale of the object. The object detection is a system that employs CNNs, is not just applying a single CNN architecture. What if the object is far away in the image? Its size will be significantly smaller than an object that is closer to the camera.

The general pattern of a CNN is that as we go through each layer, the image is shrinking, and therefore the features detected in the image become proportionally larger with every layer. The question that naturally arises is how this fact can be explored to cope with different scales in object detection. The idea is to think about sub-parts of a CNN as feature extractors instead of considering the entire CNN as a feature extractor. The output of the sub-parts of the CNN can be fed into mini-neural networks. The mini-neural networks do object detection only for a region of the CNN layers. This is the idea behind SSD algorithm and such an architecture is shown in the figure below.

CNN architecture augmented with mini-neural networks

An implementation of SSD can be found at https://github.com/balancap/SSD-Tensorflow

Object detection with SSD

Object detection employing Deep Learning

SSD introduction

Algorithm and network architecture in brief

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Object detection with SSD

Object detection employing Deep Learning

SSD introduction

Algorithm and network architecture in brief

This and many more topics are covered in our Deep Learning and Computer Vision hands-on training

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