Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.

Image segmentation is a significant step in image analysis and computer vision. Many entropy based approaches have been presented in this topic; among them, Tsallis entropy is one of the best performing methods. However, 1D Tsallis entropy does not consider make use of the spatial correlation information within the neighborhood results might be ruined by noise. Therefore, 2D Tsallis entropy is proposed to solve the problem, and results are compared with 1D Fisher, 1D maximum entropy, 1D cross entropy, 1D Tsallis entropy, fuzzy entropy, 2D Fisher, 2D maximum entropy and 2D cross entropy. On the other hand, due to the existence of huge computational costs, meta-heuristics algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization algorithm (ACO) and differential evolution algorithm (DE) are used to accelerate the 2D Tsallis entropy thresholding method. In this paper, considering 2D Tsallis entropy as a constrained optimization problem, the optimal thresholds are acquired by maximizing the objective function using a modified chaotic Bat algorithm (MCBA). The proposed algorithm has been tested on some actual and infrared images. The results are compared with that of PSO, GA, ACO and DE and demonstrate that the proposed method outperforms other approaches involved in the paper, which is a feasible and effective option for image segmentation.

Advances in technology have made people’s lives more prosperous. However, the increase in the number of cars and the emergence of autonomous driving technology have led to frequent road accidents. Manual observation of traffic conditions requires high labor intensity, low work efficiency, and poses safety risks. The paper proposes a deep learning-based pedestrian-vehicle detection model to replace manual observation, overcoming human resource limitations and safety concerns. The model optimizes the darknet53 backbone feature extraction network, reducing parameters and improving feature extraction capabilities, making it more suitable for pedestrian-vehicle scenarios. In addition, the PVFPN multi-scale feature fusion method is used to facilitate information exchange between different feature layers. Finally, the Bytetrack method is used for target counting and tracking. The paper model shows excellent performance in pedestrian-vehicle detection and tracking in traffic scenarios. The experimental results show that the improved model achieves a mAP@.5 of 0.952 with only 32% of the parameters compared to YOLOv8s. Furthermore, the proposed PVNet model, combined with the Bytetrack method, maintains high detection accuracy and is applicable to pedestrian-vehicle detection and tracking in traffic scenarios. In summary, this section discusses the traffic issues arising from technological development and presents the optimization and performance of the deep learning-based pedestrian-vehicle detection model, along with its potential applications in traffic scenarios.