A New Vehicle Detection Method based on Color Integral Histogram

An adaptive confidence algorithm for vehicle detection is proposed. Shortcomings of traditional vehicle detection method of deep learning with fixed confidence threshold are analyzed. To deal with false alarms and false negatives brought by these shortcomings, an adaptive confidence threshold algorithm for vehicle detection is raised. Two datasets are set up to test and compare the detection performance of the adaptive confidence algorithm with fixed confidence on MS-CNN. The experimental results show that the proposed algorithm could effectively reduce the false alarm rate and improve the detection performance of MS-CNN. It is of certain significance for the application of deep learning technology to vehicle detection.

Existing machine-learning-based vehicle detection algorithms for intelligent vehicles have an obvious disadvantage in that the detection effect decreases dramatically when the distribution of training samples and the scene target samples do not match. To address this issue, a scene-adaptive vehicle detection algorithm based on a composite deep structure is proposed in this paper. Inspired by the Bagging (Bootstrap aggregating) mechanism, multiple relatively independent source samples are first used to build multiple classifiers and then voting is used to generate target training samples with confidence scores. The automatic feature extraction ability of deep convolutional neural network is then used to perform source-target scene feature similarity calculations with a deep auto-encoder in order to design a composite deep-structure-based scene-adaptive classifier and its training method. Experiments on the KITTI data set and a data set captured by our group demonstrate that the proposed method performs better than existing machine-learning-based vehicle detection methods. In addition, compared with existing scene-adaptive object detection methods, our method improves the detection rate by an average of approximately 3%.

With the rapid development of intelligent transportation systems, vehicle detection and traffic flow statistics have become increasingly critical in traffic management and the construction of smart cities. However, complex weather conditions lead to challenges in vehicle detection algorithms, increasing false negatives and false positives. Additionally, the rapid growth of vehicle ownership makes traditional algorithms inadequate for meeting practical application needs. Therefore, this paper proposes a collaborative optimization-based method combining YOLOv11 for vehicle detection and traffic flow analysis under complex weather conditions. Vehicle information is accurately extracted through object detection and tracking methods, and further analyzed using Logistic Regression to assess traffic congestion. The main contributions of this study are: (1) Integration of the CBAM attention mechanism into YOLOv11, enhancing the vehicle feature extraction ability through collaborative channel and spatial attention, thereby improving interference resistance under complex weather conditions. (2) Adoption of the Shape-IoU loss function, with dynamic weight allocation to optimize the accuracy of detecting smaller vehicles and the precision of bounding box regression in images where vehicles are located at a greater distance. (3) Construction of a traffic congestion prediction model based on the detection results, integrated with Logistic Regression. The proposed method's effectiveness is validated on the public dataset UA-DETRAC. Through the synergistic effect of the CBAM attention mechanism and Shape-IoU loss function, the improved model's mAP50-95 shows a 0.3 percentage point increase over the YOLOv11 baseline, providing efficient and reliable technical support for intelligent transportation systems. This study enhances the prediction performance of traffic states under complex weather conditions by effectively calculating traffic flow parameters such as vehicle count and density, thus offering foundational data support for traffic management decision-making. The research conclusions are of practical significance for urban traffic improvement.

The majority of color histogram equalization methods do not yield uniform histogram in gray scale. After converting a color histogram equalized image into gray scale, the contrast of the converted image is worse than that of an 1-D gray scale histogram equalized image. We propose a novel 3-D color histogram equalization method that produces uniform distribution in gray scale histogram by defining a new cumulative probability density function in 3-D color space. Test results with natural and synthetic images are presented to compare and analyze various color histogram equalization algorithms based upon 3-D color histograms. We also present theoretical analysis for nonideal performance of existing methods.

This research paper introduces an intelligent parking management system aimed at optimizing parking space utilization. The system's main objective is to first keep a track of all the incoming and leaving vehicles within the parking area, while also updating the track of all the occupied and available parking slots at the same time, effectively managing the traffic. A camera installed at the entry gate will recognise the license plate and will be segmented and processed through optical character recognition achieving the recognition accuracy of 95.8% The vehicle number is then stored in a database where entry and exit times are also stored. The extracted license number will then be linked to an available parking slot that would be closest to the said vehicle, calculated through algorithms such as Dijkstra. Finally, the automatic credit system will calculate the cost through entering and leaving time, and the amount will automatically be deducted from the app's wallet. This integrated approach aims to significantly improve parking efficiency compared to manual systems. The paper will discuss the system's design, implementation, and potential performance outcomes for vehicle detection, license plate recognition, and parking slot availability. . KEYWORDS :Computer vision, Character recognition, YOLO, Vehicle detection, OpenCV

This paper introduces a high-performance vehicle detection algorithm for a parking management system based on wireless sensor technology. The parking lot is represented as a wireless sensor network cluster modeled as an m n grid in a rectangle of size l x h. A wireless sensor node is deployed in each grid and one gateway is set for the cluster. The status of each parking space is detected by a sensor node and the result is reported to the gateway. To reduce the false positive rate caused by various sources of interference, we developed a high-performance vehicle detection algorithm to filter out the noises from the passing-by cars and the environment. The performance of the algorithm is verified through extensive simulation. The three scenarios of low, medium and high interference levels are simulated. Results suggest that our proposed vehicle detection algorithm can effectively detect the status of parking lot with zero false positive rate at the low and medium interference levels, and 3.3% at high interference level.

Recent research progress in shadow detection has leveraged the development of remote sensing and computer vision. Since shadows of buildings, trees, bridges in one image can provide useful information about the scene to help people understand the shape, feature or estimate their locations and orientations of original objects, especially for damaged objects. In this study, a novel shadow detection algorithm for remote sensing imagery, called self-supervised learning method is proposed. The aim of this work is to generate shadow ratio threshold automatically without human interaction. To alleviate the traditional drawbacks of shadow detection, we fully combine supervised and unsupervised shadow detection method to suggest a self-supervised learning method, which supports us a strongly clue with establishing the relation of shadow and its original object. Subsequently, we benefit from gray-scale histogram to extract shadow segments, then shadow outlines are obtained. Finally, we assess the shadow detection performance of the proposed approach by comparing our results with the state-of-the-art methods. The results reveal the applicability and precision of the proposed self-supervised learning shadow detection technique.

In the present study, image histogram features of both single and multi channel images were used to detect and classify the common household/office smoke and other nano-scale airborne particles. To capture the Rayleigh scattered light from the particles, a white LED which covers most of the wavelengths of visible range was used as the light source. By considering the selective colour scattering behaviour of Rayleigh scattering, the intensity variation of red, green and blue components of a scattered light by the test particles are investigated using the image histograms. Particles were classified using feature vectors constructed using the running mean and mode of the histogram maximum value index (MVI) over a window of a fixed number of frames. Two classifiers: multiple discriminant analysis (MDA) and K-nearest neighbour algorithm (for comparison) were used to classify the feature vectors. Results showed that all the test particles can be detected and classified using a white LED, which is a significant step up over detecting smoke with traditional infrared LED light detectors. Even though, the feature vectors extracted with the proposed method from both colour and single channel image histograms classified all the monotype test particles with 100% accuracy with the both classifiers, the fastest classification was obtained with MVI of single channel images classified with MDA.

Lane vehicle detection is fundamental to vehicle driving systems and self-driving. The proposed concept is to employ the pixel difference in the intended lane line backdrop to isolate the lane and the road surface, and then, the curve fitting model is used to identify the lane in the image. A histogram on gradient, histogram graph, and binary spatial features are extracted from the vehicle and non-vehicle images. For vehicle detection, support vector machine classifier is employed to separate the vehicle and non-vehicle images using the extracted features. But many methods are constrained by light conditions and road circumstances, such as weak light, fog, rain, etc., which may result in invisible lane lines. Feature extraction is the lane images being picked using various filters. Our work focuses on a lane detection technique founded on the Sobel filter and curve fitting model for lane line tracking in different conditions. Preprocessing encompasses the mitigation of noise as well as getting the image ready for the subsequent procedure. To achieve this, HLS color space was performed which identifies the lane by adding pixel values. The main aim is to increase the accuracy and reduce the computation time compared to other existing methods.KeywordsSobel filterCurve fitting modelLane detectionVehicle detectionSliding windowSupport vector machine

Recently, with the explosive increase of automobiles in cities, parking problems are serious and even worsen in many cities. This paper proposes a street parking system (SPS) based on wireless sensor networks. The system can monitor the state of every parking space by deploying a magnetic sensor node on the space. For accurately detecting a parking car, a vehicle detection algorithm is proposed. And an adaptive sampling mechanism is used to reduce the energy consumption. Eighty-two sensor nodes are deployed on the street parking spaces to evaluate the performance of SPS. By running the system for more than one year, we observed that the vehicle detection accuracy of the SPS is better than 98%, and the lifetime of the sensor node is more than 5 years with a pair of 2500 mAh Li batteries.

Detecting a vehicle to obtain traffic information at nighttime is difficult. This study proposes a vehicle detection algorithm, called the headlight extraction, pairing, and tracking (HLEPT) algorithm, which can acquire traffic information in the rain at nighttime by identifying vehicles through the location of their headlights and other indicative lights. A knowledge-based connected-component procedure, in which vehicles are located by grouping their lights and estimating their features, is proposed. The features of a complex nighttime traffic scene were also analyzed. The HLEPT algorithm includes a headlight extraction algorithm, as well as regulations for the pairing and grouping of lights and light tracking using a Kanade-Lucas-Tomasi tracker to measure traffic flow and velocity. Experimental results demonstrate the feasibility and effectiveness of the proposed approach on vehicle detection in the rain at nighttime.

In order to ensure the safety of driving, this paper studies the vehicle detection algorithm based on convolutional neural network which integrates color and depth images. This paper mainly discusses the subject through convolutional network multi-scale forward looking depth imaging positioning model, forward looking variable scale vehicle detection pre-positioning algorithm, and typical model identification algorithm based on transfer learning, and verifies the algorithm performance and model identification algorithm accuracy by combining with simulation test. The results show that the accuracy of the algorithm is 96.89% under different weather conditions, indicating its effectiveness in application. Compared with previous vehicle detection algorithms, the detection accuracy of this algorithm is effectively improved by 3.96%, indicating that this algorithm has innovative significance.

Low-light image enhancement (LLIE) explores how to refine the illumination and obtain natural normal-light images. Current LLIE methods mainly focus on improving the illumination, but do not consider the color consistency by reasonably incorporating color information into the LLIE process. As a result, color difference usually exists between the enhanced image and ground-truth. To address this issue, we propose a new deep color consistent network termed DCC-Net to retain the color consistency for LLIE. A new "divide and conquer" collaborative strategy is presented, which can jointly preserve color information and enhance the illumination. Specifically, the decoupling strategy of our DCC-Net decouples each color image into two main components, i.e., gray image plus color histogram. Gray image is used to generate reasonable structures and textures, and the color histogram is beneficial for preserving the color consistency. That is, they both are utilized to complete the LLIE task collaboratively. To match the color and content features, and reduce the color consistency gap between enhanced image and ground-truth, we also design a new pyramid color embedding (PCE) module, which can better embed color information into the LLIE process. Extensive experiments on six real datasets show that the enhanced images of our DCC-Net are more natural and colorful, and perform favorably against the state-of-the-art methods.

The exponential growth of user-generated video content necessitates efficient summarization systems for improved accessibility, retrieval, and analysis. This study presents and benchmarks a multimodal video summarization framework that classifies segments as informative or non-informative using audio, visual, and fused features. Sixty hours of annotated video across ten diverse categories were analyzed. Audio features were extracted with pyAudioAnalysis, while visual features (colour histograms, optical flow, object detection, facial recognition) were derived using OpenCV. Six supervised classifiers—Naive Bayes, K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, and XGBoost—were evaluated, with hyperparameters optimized via grid search. Temporal coherence was enhanced using median filtering. Random Forest achieved the best performance, with 74% AUC on fused features and a 3% F1-score gain after post-processing. Spectral flux, grayscale histograms, and optical flow emerged as key discriminative features. The best model was deployed as a practical web service using TensorFlow and Flask, integrating informative segment detection with subtitle generation via beam search to ensure coherence and coverage. System-level evaluation demonstrated low latency and efficient resource utilization under load. Overall, the results confirm the strength of multimodal fusion and ensemble learning for video summarization and highlight their potential for real-world applications in surveillance, digital archiving, and online education.

Attributable to global economic development and rapid urbanization, traffic explosion has been observed. This situation becomes very prominent as the traditional traffic control systems are incapable to efficiently monitor and control the traffic. Therefore, this paper presents automatic vehicle detection from satellite images using deep learning approaches. For this purpose, two most renowned and widely used detection algorithms (YOLOv4 and YOLOv3) have been employed to develop satellite image vehicle detector using publicly available DOTA dataset. This work confirms the supremacy of YOLOv4 over YOLOv3 by large improvements in mAP, IOU, precision, recall, F1-score with increase of 45%, 20%, 11.1%, 45.9%, and 29.5%, respectively. Therefore, these investigational results verify the strength of YOLOv4 algorithm for satellite image vehicle detection and recommend its use for the development of an intelligent traffic control system.KeywordsDeep learningObject detectionIntelligent traffic controlVehicle detectionYOLOv3YOLOv4 algorithm