Harnessing Infrared Thermal Imaging for Safer Night Driving
Infrared tech enhances night driving safety: preprocessing, HOG+SVM recognition boost detection to 87%+. Discover Robofinity Insightdrive solutions.
The Rise of Infrared Thermal Imaging
In recent years, the global number of cars has increased explosively. According to the International Association of Car Manufacturers, in the past decade alone, the global car count has soared from around 1 billion to 1.4 billion.
With the rapid rise in car numbers, traffic accident rates remain high. This is especially true at night and in bad weather like rain, fog, or sandstorms. In particular, when driving on rural roads at night, the lack of streetlights, glare from oncoming headlights, and sudden smog can severely affect drivers’ ability to see pedestrians. This makes night driving very dangerous.
Infrared thermal imaging technology can capture target information accurately in low-visibility conditions like night or smog. Unlike visible light imaging, infrared light is invisible to the human eye. It needs special equipment to convert the infrared energy from objects into visible images.
Active infrared cameras emit infrared light and capture the reflected light to form images. Passive infrared cameras directly capture the infrared energy radiated by objects. In vehicles, passive infrared cameras are better. They work well for objects with clear temperature differences and don’t need extra light sources. This avoids revealing the vehicle’s position or disturbing other road users.
In vehicles, a passive single-lens far-infrared camera captures video sequences ahead of the car. After filtering out noise and enhancing the images, the system significantly improves the accuracy of pedestrian detection.
When the system detects pedestrians ahead, it can warn the driver in time. This reduces the risk of accidents. The application of this technology not only greatly improves night driving safety but also provides important support for the development of intelligent driving technology. It has immeasurable significance for advancing the intelligence of the entire transportation industry.
Infrared Image Preprocessing: The Key to Improving Image Quality
Hybrid Filtering Algorithm: Effective Noise Suppression
Infrared images are highly susceptible to salt-and-pepper noise and Gaussian noise during acquisition. These noises severely impact subsequent image analysis and processing. Traditional mean filtering can suppress Gaussian noise to some extent. But it blurs image textures and loses important details. Median filtering effectively removes salt-and-pepper noise. However, its performance on Gaussian noise is unsatisfactory.
To overcome the limitations of single filtering algorithms, this paper proposes an improved hybrid filtering algorithm. The algorithm first classifies the image pixels into two categories by setting reasonable upper and lower thresholds. We identify pixels greater than the upper threshold or less than the lower threshold as salt-and-pepper noise and process them using median filtering. Meanwhile, we handle Gaussian noise by applying mean filtering to pixels that fall between these thresholds.
For example, in practical applications, when we set the lower threshold to 10 and the upper threshold to 240, we replace pixels with values less than 10 or greater than 240 with the median of their neighborhoods using median filtering. We replace pixels between 10 and 240 with the average value of their neighboring pixels using mean filtering.
Experimental results show that this hybrid filtering algorithm effectively suppresses noise while better preserving image edge information. This lays a solid foundation for subsequent image enhancement.
Adaptive segmented nonlinear transformation algorithm: highlighting the target region
Infrared images generally have low contrast and unobvious target areas. This makes it difficult to distinguish targets like pedestrians from the background. Traditional piecewise linear transformation algorithms require manually determining breakpoints based on experience. This method is not flexible or universal as it can’t adapt to various images in different scenes.
The proposed boxplot-based adaptive piecewise nonlinear transformation algorithm sorts image grayscale values to automatically determine the concentrated distribution range of grayscales. Thus it can adaptively select breakpoint parameters. Specifically, it first uses the principle of boxplot to determine the breakpoints of the concentrated grayscale area. These breakpoints reflect the grayscale distribution characteristics of the image.
Then, select appropriate adjustment points outside the right side of the grayscale distribution interval. Generate a smooth nonlinear transformation curve using cubic spline interpolation. This curve can transform pixels in different grayscale ranges based on image characteristics. It effectively suppresses the grayscale values in the background area while enhancing the grayscale contrast in the target pedestrian area. This makes the target more prominent and greatly improves the image contrast and clarity.
Compared with traditional algorithms through experiments, the algorithm in this paper has significant advantages in adaptive nonlinear transformation and enhancement effects when processing the same infrared images. It can increase the distinguishability between the target and the background by more than 30%.
Infrared Pedestrian Recognition Algorithms: The Core of Accurate Detection
Feature extraction: application of HOG features
Researchers commonly use HOG (Histogram of Oriented Gradients) features to describe pedestrians in infrared recognition systems. They effectively describe human contours and edge information. The basic principle is to divide the image into smaller cells. We calculate the gradient orientation histograms of pixels in each cell. Then, we combine adjacent cells into blocks. Finally, we normalize the histograms in the blocks. This forms a vector that describes the image features.
In infrared images, HOG features adapt well to their low resolution and low contrast. By calculating pixel gradient information, HOG features highlight pedestrian contours. Even with poor image quality, they can accurately capture pedestrian shapes. Compared to LBP(Local Binary Patterns)features, HOG features cover a larger area. They are more suitable for the scale requirements of pedestrian detection. For example, when detecting distant pedestrians, HOG features can describe the overall contour more comprehensively. LBP features may fail due to limited local information.
Classifier Training: PSO Optimized SVM Algorithm
Researchers and developers widely use the Support Vector Machine (SVM) as an efficient binary classification model in pattern recognition. However, traditional SVM’s parameters, the penalty factor C and Gaussian kernel parameter γ,are hard to optimize manually. This greatly affects classification accuracy.
This paper introduces Particle Swarm Optimization(PSO)to optimize SVM parameters. PSO simulates birds’ foraging behavior. Each particle is a potential solution in the solution space. Particles fly and share information to gradually approach the optimal solution. During training, SVM is first trained with initial parameters. Then PSO adjusts parameters C and γ.Each particle updates its velocity and position based on its own experience and the best particle’s position in the group. This helps find the parameter combination that maximizes SVM classification accuracy.
Experiments show that PSO-optimized SVM significantly improves classification accuracy for infrared pedestrian images. It increases accuracy from around 75%to over 90%,compared to traditional manually adjusted SVM.
Detection Optimization: NMS and Video Sequence Processing
During pedestrian detection, using multi-scale sliding window detection will inevitably cause the same target to be marked by multiple detection boxes. This not only affects the accuracy of the detection results but also reduces their readability.
The Non-Maximum Suppression(NMS)algorithm calculates the overlap ratio between detection boxes to determine which ones are duplicates. It removes the redundant boxes and keeps only the best one. For example, if the overlap area between two boxes exceeds a set threshold(such as 0.5),they are considered to detect the same target. In this case, the box with the higher score is kept, and the one with the lower score is deleted.
For video sequence processing, to improve detection efficiency, this paper adopts the optimization strategies of region-limited detection and frame-skipping detection. Based on the characteristics of pedestrian appearance in vehicle-mounted scenarios, it excludes the redundant areas at the top(sky area)and bottom(ground area near the vehicle)of the video. Detection is only performed in the middle area where pedestrians may appear. This greatly reduces the computational load.
At the same time, frame-skipping detection is used to reduce the frame rate. Detection is performed every one or several frames. Under the premise of basically not affecting the detection effect, the system’s real-time performance is significantly improved. This allows the system to respond quickly in complex driving environments.
System Construction and Experimental Verification: From Theory to Practice
Hardware Platform Construction
The hardware of the in-vehicle driving assistance system built in this paper mainly consists of an in-vehicle infrared camera, a video capture card and an in-vehicle industrial computer. The in-vehicle infrared camera chosen has high sensitivity and resolution. It can clearly capture the infrared video sequence in front of the vehicle. The video capture card is responsible for stably transmitting the signal captured by the infrared camera to the in-vehicle industrial computer. The in-vehicle industrial computer is equipped with the Ubuntu 16.04 operating system. It has strong computing power and can run complex image preprocessing and pedestrian recognition algorithms.
In addition,to ensure the stability of the system during vehicle driving,the hardware devices have been specially designed and reinforced for anti-vibration and dust-proof.
Software environment and porting
During the software development phase, we use the Windows system as the development environment. We develop algorithms in Python using PyCharm and Miniconda. Python’s rich libraries and concise syntax facilitate algorithm implementation.
After algorithm development, the system needs to be ported to an in-vehicle industrial computer with Ubuntu 16.04. This involves exporting project dependencies to ensure all libraries and modules work in the new environment. We also modify path information to fit the in-vehicle computer’s file system structure. Additionally, we install the required libraries. Through these steps, we successfully achieve cross-platform system operation, ensuring its stability and reliability in the in-vehicle environment.
Analysis of experimental results
To verify the effectiveness of the proposed algorithm, numerous infrared video sequences were tested. Results show that the combination of the proposed image enhancement algorithm and PSO-optimized SVM outperforms traditional methods in detection and false alarm rates. In multiple experiments, the detection rate(TPR)reached over 87%and the false alarm rate(FPR)was below 8%.Traditional methods have a detection rate of 70%-80%and a false alarm rate of 15%-20%.
Additionally, with region limitation and frame-skipping detection, the system achieves 29 frames per second. This meets real-time requirements and allows timely detection and warning of pedestrians during vehicle travel. Distance tests show good recognition within 50 meters. As distance increases, targets shrink and features are lost, reducing recognition effectiveness. Beyond 50 meters, the detection rate drops to around 70%. Considering detection effectiveness and practical needs,50 meters is set as the effective detection range.
The Future of Infrared Thermal Imaging and Technical Recommendations
We’ve done a lot of work on using infrared thermal imaging in car driving assistance. This includes image preprocessing, pedestrian detection algorithms and system building. We proposed a hybrid filtering algorithm, an adaptive piecewise nonlinear transformation algorithm, and a HOG+SVM+PSO+NMS detection algorithm. These optimize infrared image processing and pedestrian detection in many ways. They improve image quality and detection accuracy. This supports the practical use of car driving assistance systems.
However, there are still some areas for improvement. For example, when pedestrians are blocked, the image enhancement is not ideal. It can cause missed or false detections. The sliding window detection has a large computation load. When dealing with high-resolution images or complex scenes, it affects the system’s real-time performance.
In future research, we can explore image enhancement methods combining erosion and dilation principles. Morphological processing of images can better restore occluded target information. We can also study region of interest search algorithms. This can reduce unnecessary calculations and improve detection efficiency. Multithreading computing techniques can be adopted. This fully utilizes the performance of multicore processors and speeds up algorithm execution. Meanwhile, we can delve into video motion information and integrate it into detection algorithms to optimize results.
With the rapid development of intelligent driving technology, to explore high-performance in-vehicle driving assistance solutions, one can focus on Robofinity Insightdrive. As an innovative industry solution, Robofinity Insightdrive combines advanced sensor algorithms and intelligent processing technologies. It provides more accurate environment perception and target detection for in-vehicle scenarios. Its performance under complex lighting conditions is remarkable.
It can process infrared images and integrate data from lidar, millimeter-wave radar and other sensors. This achieves multimodal information fusion and offers more comprehensive and accurate environment perception. For developers or industry practitioners who want to explore the integration of infrared technology and intelligent driving, Robofinity Insightdrive is a cutting-edge technology worth paying attention to. Its technical concepts and practical achievements may bring new inspiration and breakthroughs to the field of in-vehicle safety.
