Application of Computer Vision 13-Application Project of Water Recognition on Urban Roads Based on SSD Model

Hello everyone, I am Weixue AI. Today I will introduce to you the application of computer vision 13-the application project of urban road water recognition based on SSD model. Affected by the clouds behind this year's No. 11 Typhoon Haikui, the rainfall in Fuzhou has exceeded the historical extreme, and many places have serious water accumulation. Water accumulation on urban roads is one of the main causes of traffic congestion, vehicle accidents and overloading of urban drainage systems. Therefore, accurately identifying water accumulation on urban roads is crucial for urban traffic management and public safety. This paper proposes a method for identifying water accumulation on urban roads based on the SSD model.
We collected a large amount of image data of water accumulation on urban roads and annotated it. Then, we use deep learning technology to input these image data into the SSD model for training. By optimizing the loss function, the accuracy of the model in identifying road water accumulation is improved. The urban road water accumulation identification method based on the SSD model has potential in practical applications and can provide useful support for urban traffic management and public safety.

Table of contents

1. Project background and significance
2. Training data example
3. SSD model introduction
4. Build SSD model
5. Model training and testing
6. Code
7. Conclusions and future work

1. Project background and significance

With the acceleration of urbanization, problems in urban infrastructure construction and maintenance have become more and more prominent, one of which is the problem of road water accumulation. When typhoons and heavy rains increase, the continuous precipitation will lead to large areas of water accumulation on the roads. The water accumulation on the roads not only affects traffic, but may also cause traffic accidents and even pose a threat to people's lives. Therefore, it is of great significance to identify and deal with road water accumulation problems in a timely and effective manner.
Traditional road water identification methods mainly rely on manual inspections, which are inefficient and cannot detect and deal with problems in real time. Therefore, we need an automated and efficient method for identifying road water accumulation. In recent years, deep learning has achieved remarkable results in the field of image recognition. In particular, the SSD model is widely used in various image recognition tasks due to its excellent target detection and recognition capabilities.
This project proposes a method for identifying road water accumulation based on the SSD model. We applied this method to road images and achieved efficient and accurate identification of road water accumulation.

2. Training data examples

To train our model, we collected a large number of road images, both with and without standing water. Each image is annotated to indicate the water accumulation area in the image.

Here are some examples of our training data:

Image1.jpg, "water", 14, 30, 56, 70
Image2.jpg, "water", 35, 50, 66, 90
Image3.jpg, "no_water", 0, 0, 0, 0
...

In the above data, each row represents an image. The first column is the image name, the second column is the label of the image ("water" means there is water, "no_water" means there is no water), and the third to sixth columns are the bounding box coordinates of the water area.

3. SSD model introduction

The SSD model is a deep learning target detection model. Compared with other target detection models, the SSD model has higher detection speed and better detection effect.

The main feature of the SSD model is that it uses multi-scale feature maps to detect targets, and uses multiple scales and aspect ratios of default bounding boxes (default boxes) on each feature map to predict targets.

The training of the SSD model mainly includes two parts: one is to regress the position of the default bounding box and adjust its matching degree with the real bounding box; the other is to classify each default bounding box to determine whether it contains the target.

The principle of the SSD model:
1. Feature extraction:
The SSD model uses a pre-trained CNN as the basic network, usually VGGNet or ResNet, etc. Given input image $x$ , a series of feature maps can be obtained through this basic network. These feature maps contain different levels of semantic information, with low-level feature maps containing local and detailed information, and high-level feature maps containing more semantic and contextual information.
2. Multi-scale feature map generation:
The SSD model adds additional convolutional layers at different levels of the basic network to generate feature maps of different scales. These additional convolutional layers are called auxiliary convolutional layers. Each auxiliary convolutional layer generates a set of feature maps, each feature map corresponding to a fixed default box. Since feature maps at different levels have different receptive fields, targets can be detected at different scales.
3. Object classification and localization:
For each default box, the SSD model predicts the class probability of the object and the location of the bounding box. Specifically, each default box will obtain a fixed-dimensional feature representation through a series of convolutional layers and fully connected layers, and then be used for classification and regression tasks respectively. The classification task uses the softmax function to calculate the probability of each category, and the regression task predicts the location and size of the bounding box.
4. Loss function:
The SSD model uses a multi-task loss function to train the model. This loss function consists of two parts: classification loss and localization loss. The classification loss uses the cross-entropy loss function to measure the prediction error of the target category, and the localization loss uses the smoothed L1 loss function to measure the prediction error of the bounding box location. The final total loss is a linear weighted sum of classification loss and localization loss.

By optimizing large-scale labeled training data, the SSD model can learn effective feature representation and object detection capabilities. The model has good performance and real-time performance in target detection tasks.

The mathematical principle expression of the SSD model:

1. Feature extraction:
   $$f=\text{CNN}(x)$$

2. Multi-scale feature map generation:
   $$d_k={\text{Conv}}_k(f)$$

3. Target classification and positioning:
   $$p_{i,k}=\text{softmax}(c_{i,k})$$
   $$b_{i,k}=\text{decode}(d_{i,k})$$

4. Syntax:
   $$L=l_{\text{cls}}{L}_{\text{cls}}+l_{\text{loc}}{L}_{\text{loc}}$$

Among them, $f$ represents the feature map,$d_k$represents the $Feature\; maps\; of\; k$ auxiliary convolutional layers,$p_{i,k}$Represents the $Class\; probability\; of\; i$ default box,$b_{i,k}$Represents the $Bounding\; box\; positions\; of\; i$ default boxes,$L_{\text{cls}}$Represents the classification loss, $L_{\text{loc}}$Represents the positioning loss, $l_{\text{cls}}$和$l_{\text{loc}}$is the weight of the loss.

4. Build SSD model

Under the PyTorch framework, we can easily build SSD models. The following is the code we use to build the SSD model:

import torch
from torch import nn
from ssd.modeling import registry
from .backbone import build_backbone
from .box_head import build_box_head

@registry.DETECTORS.register('SSD')
class SSD(nn.Module):
    def __init__(self, cfg):
        super(SSD, self).__init__()
        self.backbone = build_backbone(cfg)
        self.box_head = build_box_head(cfg)

    def forward(self, images, targets=None):
        features = self.backbone(images)
        detections, detector_losses = self.box_head(features, targets)
        if self.training:
            return detector_losses
        return detections

In the above code, we first define an SSD class, which inherits from nn.Module. In the constructor of the SSD class, we construct the backbone and box_head parts. The backbone part is used to extract features of the image, and the box_head part is used to detect targets from the features. In the forward function of the SSD class, we first extract the features of the image through backbone, and then detect the target from the features through box_head. If it is the training phase, we return the detection loss; if it is the testing phase, we return the detection result.

5. Model training and testing

Model training includes the following steps:

1. Read the training data
2. Pass the image into the model and get the detection loss
3. Use the optimizer to optimize the loss and update the parameters of the model
4. Repeat the above steps until the performance of the model reaches a satisfactory level

Testing of the model includes the following steps:

1. Read the test data
2. Pass the image into the model and get the detection result
3. Compare it with the real result and calculate the performance index of the model
4. Repeat the above steps to test all test data

6. Code implementation

Code implementation for training and testing our model:

import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from dataset import WaterDataset
from model import SSD
from loss import SSDLoss

# 读取数据
dataset = WaterDataset('data/train.csv')
data_loader = DataLoader(dataset, batch_size=32, shuffle=True)

# 构建模型
model = SSD()
model = model.to('cuda')

# 定义损失函数和优化器
criterion = SSDLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
for epoch in range(100):
    for images, targets in data_loader:
        images = images.to('cuda')
        targets = targets.to('cuda')

        # 前向传播
        loss = model(images, targets)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# 测试模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, targets in data_loader:
        images = images.to('cuda')
        targets = targets.to('cuda')

        # 前向传播
        outputs = model(images)

        # 计算准确率
        total += targets.size(0)
        correct += (outputs == targets).sum().item()

    print('Test Accuracy: {}%'.format(100 * correct / total))

7. Conclusions and future work

This project proposes a road water recognition method based on the SSD model. Through training on a large number of road images, efficient and accurate road water recognition is achieved. However, our approach has some limitations. For example, our method relies on high-quality training data, and the acquisition and annotation of these data is a time-consuming and difficult process. In addition, our method may have some difficulties when dealing with water recognition in complex scenes (such as rainy days, nights, etc.).

In the future, we will further optimize our model to improve its ability to identify water accumulation in complex scenarios. We also plan to collect and label more training data to improve the generalization ability of our model. At the same time, we will also explore other deep learning models to improve our water recognition effect.