Road traffic flow detection equipment based on ResNet-50 convolutional neural network
By applying the intelligent identification method of ResNet-50 convolutional neural network in traffic flow detection, the problem of intelligent and efficient identification in the existing technology is solved, and the effect of accurately counting the number of vehicles and assessing traffic congestion in complex road environments is achieved. .
Patent Information
- Application Number
- CN202310883340.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-07-18
- Publication Date
- 2025-01-21
AI Technical Summary
Existing vehicle flow detection technology is difficult to achieve intelligent and efficient identification, especially in complex road environments, and it is difficult to accurately count the number of vehicles and evaluate traffic congestion.
An intelligent identification method based on ResNet-50 convolutional neural network is used to collect road videos through image acquisition equipment. After preprocessing, artificial intelligence algorithms are used to intelligently identify vehicles and count the number of vehicles to obtain traffic flow information.
It realizes efficient and intelligent identification of vehicles in complex road environments, accurately counts traffic flow information, helps evaluate traffic conditions and congestion levels, and improves the efficiency and accuracy of traffic management.
Smart Images
Figure CN119339335A_ABST
Abstract
Description
Technical Field
[0001] The present invention is applied to the field of road vehicle flow detection, and in particular relates to a road vehicle flow detection device based on a ResNet-50 convolutional neural network. Background Art
[0002] Traffic flow detection refers to the process of evaluating traffic flow and congestion by monitoring the number, speed, density, direction of movement and other parameters of vehicles on the road. Traffic flow detection is of great significance in traffic management and urban planning. Through traffic flow detection, traffic flow information at different time periods and locations on the road can be obtained, thereby helping traffic planners and designers understand the use of roads. This helps to optimize the traffic network, road layout and signal light settings, and improve the traffic efficiency and safety of roads. Traffic flow detection can help identify the location and degree of traffic congestion. Based on this information, the traffic management department can take corresponding measures, such as adjusting the timing of signal lights, increasing lane capacity, guiding traffic flow, etc., to alleviate congestion and improve traffic flow. The road traffic flow detection equipment of the present invention can intelligently identify the types of vehicles on a certain road, and then count the traffic flow information of this road in a certain time period. ResNet50 is a very powerful deep learning model. Its depth and residual learning methods enable it to learn more complex features, thereby improving its accuracy. Its global average pooling layer can reduce the number of parameters of the model, thereby reducing the risk of overfitting. ResNet50 has been widely used in the field of computer vision, such as image classification, object detection, and semantic segmentation. Summary of the invention
[0003] To achieve the above object, the present invention is implemented through the following technical solutions:
[0004] A road traffic flow detection device based on a ResNet-50 convolutional neural network is characterized in that the method comprises the following steps: Step 1: using an image acquisition device to shoot a road that needs to be detected for traffic flow; Step 2: preprocessing the video and picture information shot by the image acquisition device; Step 3: intelligently identifying the cars in the captured videos and photos through an artificial intelligence algorithm; Step 4: performing traffic flow statistics and analysis based on the number of detected vehicles.
[0005] As a further solution of the present invention, step 1 specifically includes the following contents: the image acquisition device takes video or images of the road that needs to be detected for traffic flow, and transmits the collected data to the data processing center through the network for preprocessing of the image information.
[0006] As a further solution of the present invention, step 2 specifically includes the following contents: the image preprocessing step includes image denoising, grayscale, image enhancement, image smoothing and sharpening.
[0007] As a further solution of the present invention, step 3 specifically includes the following contents: after the vehicles on the road are trained for image recognition based on the ResNet-50 convolutional neural network, the images of the vehicles traveling on the road can be recognized by the training results to obtain the road traffic information.
[0008] As a further solution of the present invention, step 4 specifically includes the following contents: according to the image recognition result of step 3, the recognized vehicles are counted to obtain the traffic flow information of the road in a certain time period.
[0008] Compared with the prior art, the present invention has the following significant advantages: the present invention obtains road traffic information, namely, vehicle speed, number of vehicles and other information, based on intelligent vehicle identification based on the ResNet-50 convolutional neural network algorithm, thereby helping to evaluate road traffic conditions and congestion levels. BRIEF DESCRIPTION OF THE DRAWINGS
[0009] Figure 1 This is a workflow diagram of a road vehicle flow detection device based on a ResNet-50 convolutional neural network of the present invention. DETAILED DESCRIPTION
[0010] The present invention will be further described in detail below through specific implementation modes in conjunction with the accompanying drawings.
[0011] The present invention provides a production line product temperature detection device based on ResNet-50 and thermal imaging technology, step 1: using an image acquisition device to shoot a road that needs to be detected for vehicle flow; step 2: preprocessing the video image information shot by the image acquisition device; step 3: using an artificial intelligence algorithm to intelligently identify the cars in the shot videos and photos; step 4: performing vehicle flow statistics and analysis based on the number of detected vehicles.
[0012] Each step is described in detail below.
[0013] Step 1: Use image acquisition equipment to photograph the road where traffic flow detection is required;
[0014] The image acquisition device is used to capture images or videos of roads that need to be monitored for traffic flow, and transmit the videos to a data processing center, thereby obtaining the traffic flow information of the road through intelligent recognition.
[0015] Step 2: Preprocess the video image information captured by the image acquisition device;
[0016] The purpose of the preprocessing is to improve the quality of the image, reduce noise, and extract useful features and information for subsequent intelligent vehicle identification. The image preprocessing steps include image denoising, grayscale, image enhancement, and image smoothing. Image denoising will remove noise from the image and make the image clearer. Grayscale converts color images into grayscale images, simplifies the image processing process, reduces the amount of calculation, and extracts texture and structural information from the image. Image enhancement will adjust the brightness, contrast, and color balance parameters of the image to improve the visual quality of the image. Image smoothing will reduce noise and details in the image and make the contour and structure of the image smoother.
[0017] Step 3: Intelligently identify cars in the captured videos and photos through artificial intelligence algorithms
[0018] The artificial intelligence algorithm of the present invention is a ResNet-50 convolutional neural network model. This model is combined with the pre-processed image information obtained after step 2 and the training results of the model to achieve the purpose of identifying vehicles on the road.
[0019] Step 4: Perform traffic flow statistics and analysis based on the number of detected vehicles.
[0020] The traffic flow statistics analysis of the present invention counts the number of vehicles passing through the road identified in step 3 during a certain period of time. In step 4, the number of vehicles is counted and analyzed to monitor the number, speed, and movement direction of vehicles on the road, and further evaluate the traffic flow and congestion conditions.
[0021] The above content is a further detailed description of the present invention in combination with specific preferred implementation modes. It cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.
Claims
1. A road traffic flow detection device based on ResNet-50 convolutional neural network, characterized in that: The method comprises the following steps: Step 1: Use image acquisition equipment to photograph the road where traffic flow detection is required; Step 2: Preprocess the video image information captured by the image acquisition device; Step 3: Intelligently identify the cars in the captured videos and photos through artificial intelligence algorithms; Step 4: Perform traffic flow statistics and analysis based on the number of detected vehicles.
2. According to claim 1, a road traffic flow detection device based on ResNet-50 convolutional neural network is characterized in that: The image acquisition device will take video or image shots of the road where the traffic flow detection is required, and transmit the acquired data to the data processing center through the network for pre-processing of the image information.
3. The road traffic flow detection device based on ResNet-50 convolutional neural network according to claim 1 is characterized in that: The preprocessing includes: the purpose of image preprocessing is to improve image quality and reduce noise, and the image preprocessing steps include image denoising, grayscale, image enhancement, image smoothing and sharpening.
4. The road traffic flow detection device based on ResNet-50 convolutional neural network according to claim 1 is characterized in that: The artificial intelligence algorithm is: a deep convolutional neural network ResNet-50, whose structure mainly includes an input layer, a convolutional layer, a residual module, a pooling layer, and a full-link layer. ResNet-50 is widely used in image classification tasks. In the present invention, it is mainly used to identify cars on the road and obtain road traffic information based on later statistics.
5. The road traffic flow detection device based on ResNet-50 convolutional neural network according to claim 1 is characterized in that: The vehicle flow statistics include: using the ResNet-50 convolutional neural network to identify and count vehicles, and calculating vehicle passing speed, density, flow and other indicators by analyzing vehicle counting data.