In this study, we propose a novel CLAHE-based nighttime image contrast enhancement approach for vehicle detection under nighttime conditions, which improves the contrast of low-quality nighttime images while preventing over-enhancement by employing the image dehazing technique. To implement and evaluate our proposed contrast enhancement method on nighttime images, we consider a scenario of using a camera-based Internet of Things (IoT)-edge computing device for traffic and road surveillance. Edge-computing and IoT technology enable significant amounts of novel studies to advance traffic system monitoring, sensing, control, and management. Considering multiple metrics of image enhancement quality, the proposed nighttime image contrast enhancement method outperforms some existing well-performing CLAHE-based methods. To provide accurate vehicle detection under nighttime conditions and different challenges, including vehicle overlapping, low-light conditions, camera vibrations, and image distortion, must be addressed. For this purpose, a deep neural network based on YOLOv5 architecture has been designed and trained using our custom-labeled dataset. The developed neural network is proven to be effective in the detection of different vehicles under low-light ambient conditions using video captured from a stationary camera. Experiments on our dataset show that the proposed contrast enhancement method greatly improves the detection performance of the trained YOLOv5 model under low-environment-light conditions compared with the model trained using unenhanced images. The model trained with enhanced images can provide an improvement of 5.7% on F1 score, 6.3% on mAP $_{0.5,}$ and 3.4% on mAP $_{0.5:0.95} $ under specific conditions.

A significant number of camera-based solutions suffer from different kinds of performance issues, caused by unstable weather conditions e.g., wind, object overlapping due to heavy traffic flow, and lack of adjustment for a certain location where the camera is installed and fixed. In this paper, we developed an effective approach to traffic flow monitoring under daytime conditions by applying machine learning and computer vision techniques to extract motion traffic data parameters from the videos captured by the static surveillance camera installed and fixed at the intersection. We address the issue of video camera vibration and jittering by applying image-matching algorithms. In order to calculate the vehicle speed, we utilize the projective transformation to compute the real object distance from an image taken by a single camera installed at the road intersection. We adapt and employ state-of-the-art object detectors, transfer learning, and analytical computer vision methods to detect and track vehicles and measure moving speed and vehicle volume from a predefined detection area extracted from monocular videos. The developed approach is proven to be effective in estimating vehicle speed and vehicle volume using video sequences captured from a stationary camera. Experimental results obtained during the study prove the efficiency of the proposed method under strong vibration conditions. The proposed framework achieved promising tracking performance on custom-labeled test video clips with a small absolute error of 3.97% for traffic flow average speed estimation. An additional test on a video from BrnoCompSpeed Dataset shows that the proposed method can reduce the average speed estimation error by 51.8%.

Accurately evaluating the intelligence of autonomous path planning remains challenging, primarily due to the interdependencies among evaluation metrics and the insufficient integration of subjective and objective weighting methods. This paper proposes Game-Theoretic Kendall’s Coefficient (GTKC) weighting framework for evaluating autonomous path planning intelligence. The framework specifies a safety–efficiency–comfort metric system with observable, reproducible, and quantifiable metrics. To account for intermetric dependence, subjective weights are elicited via an improved Analytic Network Process (ANP), while objective weights are derived using the CRITIC method to capture contrast intensity and intercriteria conflict. The credibility of the subjective and objective weights is evaluated using Kendall’s coefficient and the coefficient of variation, respectively. Subsequently, based on the principle that higher credibility should receive greater weight, a game-theoretic optimization model is employed to dynamically derive optimal combination coefficients. Experimental results on three case scenarios demonstrate that the GTKC framework significantly outperforms existing weighting approaches in terms of effectiveness (achieving a lowest Mean Absolute Error (MAE) of 0.15 and a perfect Spearman’s correlation coefficient ($\overline{\rho }=1.0$) with ground-truth rankings), stability (Mean Standard Deviation (MSD) = 0.023), and ranking consistency (Kendall’s coefficient W = 0.924). These findings validate GTKC as a theoretically grounded and practically robust mechanism that explicitly models metric interdependencies and integrates expert knowledge with empirical evidence, enabling reliable and reproducible evaluation of autonomous path planning intelligence.