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AI-Based Methods for Object Detection and Path Planning

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A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 April 2026) | Viewed by 21929

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Prof. Dr. Ping Lou

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School of Information, Wuhan University of Technology, Wuhan 430070, China
Interests: industrial big data; industrial intelligence; digital twins; multi-agent modeling and simulation
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Special Issue Information

Dear Colleagues,

With the advent of advanced technology, artificial intelligence (AI) is being widely used across various domains, with significant applications in object detection and path planning, including warehousing logistics, urban transportation, military operations, automatic driving, robotics, and drones. Through the integration of advanced AI technologies, including reinforcement learning, graph neural networks, multi-agent systems, and large language models, these methods significantly enhance the accuracy, efficiency, and adaptability of object detection and path planning in complex environments.

We are therefore interested in articles that investigate AI-based methods for object detection and path planning. Potential topics include, but are not limited to, the following:

Path optimization in multimodal transportation systems.
Path planning in dynamic environments and under uncertainty.
The application of reinforcement learning in path planning in complex environments.
Coordinated path planning in multi-agent systems.
Spatio-temporal graph neural networks for path planning.
The uses of large language models in path planning and object detection.
Advanced artificial intelligence technology in multi-modal object detection.
Advanced artificial intelligence technology in real-time object detection.
The applications of advanced AI-based object detection and path planning in complex environments such as warehousing logistics, urban transportation, military applications, automatic driving, robotics, and drones.

Prof. Dr. Ping Lou
Guest Editor

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Keywords

artificial intelligence
multi-agent systems
path planning
graph neural networks
large language models
real-time object detection
multi-modal object detection

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Related Special Issue

AI-Based Methods for Object Detection and Path Planning, 2nd Edition in Applied Sciences

Published Papers (10 papers)

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Research

28 pages, 10841 KB

Open AccessArticle

Attention-Enhanced YOLOv26 with Tree-Structured Parzen Estimator Optimization for Robust Dental Surgical Tool Detection

by Mehmet Burukanli, Musa Cibuk and Davut Ari

Appl. Sci. 2026, 16(15), 7654; https://doi.org/10.3390/app16157654 - 1 Aug 2026

Viewed by 421

Abstract

Object detection remains a fundamental challenge in computer vision and plays a pivotal role in safety-critical medical applications, including surgical instrument recognition and operating-room workflow automation. This study presents a comprehensive comparative evaluation of five attention mechanisms—Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), Efficient Channel Attention (ECA), Simple Attention Module (SimAM), and an enhanced multi-kernel Spatial Pyramid Pooling Fast module (SPPF+)—integrated into the YOLOv26n backbone, together with two neck-level attention variants (ECA-Neck and CBAM-Neck). A total of 16 model configurations were systematically investigated on a 22-class dental surgical instrument detection dataset under both default training settings and hyperparameter configurations optimized using the Optuna Tree-structured Parzen Estimator (TPE), enabling a rigorous full-factorial ablation study. Experimental results demonstrate that TPE-based hyperparameter optimization consistently enhances detection performance across all architectures. Among the evaluated models, CBAM-Opt achieved the highest detection accuracy, attaining an mAP@50 of 0.959 and an F1-score of 0.913, although the margins among the top optimized configurations fall within run-to-run variability. In contrast, Base-Opt delivered the strongest strict-localization capability with an mAP@50–95 of 0.800, highlighting the competitive performance of the baseline architecture when appropriately optimized. Notably, the parameter-free SimAM module exhibited the largest improvement following optimization (

Δ

mAP@50 = +0.040), indicating a pronounced sensitivity to training configuration. Furthermore, neck-level attention integration achieved performance comparable to backbone-based attention, with ECA-Neck-Opt reaching an mAP@50 of 0.959, suggesting an effective alternative that preserves pretrained feature representations while maintaining high detection accuracy. Beyond performance evaluation, this work provides a unified benchmarking framework for attention mechanisms in medical object detection, accompanied by computational complexity analysis and practical architectural insights. The findings establish evidence-based guidelines for selecting attention modules in resource-aware surgical vision systems and contribute toward the development of more accurate and reliable computer-assisted clinical workflows. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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26 pages, 18175 KB

Open AccessArticle

MobileMamba-DETR: Efficient Dual-Modal Vehicle Detection for Autonomous Driving via Multi-Scale Selective State Space Fusion

by Bo Li, Chunhao Li and Yuheng Li

Appl. Sci. 2026, 16(12), 5998; https://doi.org/10.3390/app16125998 - 13 Jun 2026

Viewed by 416

Abstract

Robust autonomous-driving detection requires using RGB texture and infrared thermal cues without sacrificing real-time inference. Existing RGB-IR detectors often rely on static feature concatenation or quadratic attention, which makes them sensitive to modality imbalance, small spatial offsets, and deployment cost. We propose MobileMamba-DETR, a lightweight DETR-style detector that treats dual-modal fusion as a selective state-space process. Its principal design is an SS2D-based cross-modal interaction module that uses normalized RGB-IR contrast as a guide, while a MobileMamba backbone, spectral–spatial encoder, and dynamic convolutional decoder provide efficient multi-scale representation and query localization. On M3FD and FLIR-ADAS, MobileMamba-DETR achieves mAP₅₀ of 83.6% and 78.3%, respectively, with 38.7M parameters and 42 FPS inference at

640 \times 640

on an RTX 3090. The results, ablations, and seed-based validation show that selective state-space fusion improves accuracy while retaining real-time throughput. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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24 pages, 9325 KB

Open AccessArticle

UAV Inspection Path Planning for Reservoir Slopes: Application of a Weighted Traveling Salesman Problem Model Based on Genetic Algorithm

by Guoliang Zhao, Dingtian Lin, Yaxin Tan, Xitong Zhang, Shence Zhang, Baoquan Yang, Junteng Wang and Xinyi Tang

Appl. Sci. 2026, 16(10), 4765; https://doi.org/10.3390/app16104765 - 11 May 2026

Viewed by 506

Abstract

Regular inspection of defects like sprayed concrete cracking and water seepage is crucial for the long-term safety of reservoir slopes in hydraulic engineering. Traditional manual inspections suffer from low efficiency and high cost. This paper presents a weighted Traveling Salesman Problem (TSP) model established by a Genetic Algorithm (GA) to optimize Unmanned Aerial Vehicle (UAV) inspection paths for these slopes. The model integrates UAV acceleration and deceleration physics. It weights the flight distance, converting it into flight time, and uses 3D-coordinate data to form the objective function. We calibrated key parameters, including acceleration and speed thresholds, by fitting displacement-time quadratic functions to field data from a DJI Matrice 350 RTK UAV. Tests on multiple slope models show the weighted GA optimizes the planned path by 46.2%, improves average inspection efficiency by 7.90% over an algorithm simulating human decision-making, and by 7.66% over a standard (non-weighted) GA. This work provides a reference for intelligent path planning on reservoir slopes and is applicable to similar scenarios like highway and railway slopes. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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23 pages, 3336 KB

Open AccessArticle

An Algorithm for Safety Helmet Detection Based on Improved YOLOv8

by Zhibo Wang, Chuankai Li, Guoming Xiong, Guoyun Zhong, Jianfeng He and Han Ma

Appl. Sci. 2026, 16(10), 4613; https://doi.org/10.3390/app16104613 - 8 May 2026

Viewed by 671

Abstract

In recent years, due to the high number of unsafe factors in the construction industry, its death rate and injury rate have become the focus of research in the field of target detection. In the complex environment of the construction site, workers often forget to wear helmets or fail to wear them properly, causing hidden dangers for the safety of workers. Therefore, this paper proposes an application-oriented improvement strategy for helmet detection based on the YOLOv8 architecture (EC-YOLOv8), which integrates the ECA attention mechanism and content-sensing recombination feature operator into the YOLOv8 network, so as to further improve detection accuracy and detection time, and better meet the actual application requirements of construction sites. Enhanced Intersection over Union Loss (EIoU Loss) is introduced to improve the network model’s ability to evaluate the difference between the predicted boundary box and the real boundary box. The experimental results show that the EC-YOLOv8 algorithm proposed in this paper achieves 95.7% accuracy on the data set SHWD, which improves the detection accuracy of the helmet target. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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31 pages, 8325 KB

Open AccessArticle

Development of a Collision Avoidance System Using Multiple Distance Sensors for Indoor Inspection Drone

by Teklay Asmelash Gerencheal and Jae Hoon Lee

Appl. Sci. 2025, 15(16), 8791; https://doi.org/10.3390/app15168791 - 8 Aug 2025

Cited by 4 | Viewed by 2990

Abstract

Drones, particularly those designed for indoor inspections, are widely used across various industries for tasks such as infrastructure monitoring, maintenance, and security. This study focuses on developing a robust collision avoidance system for teleoperated indoor drones, ensuring comprehensive 360-degree horizontal safety during flight. By integrating multiple cost-effective and compact Time-of-Flight (ToF) distance sensors, the system enhances real-time obstacle detection and collision prevention. A custom sensor module, strategically installed on the drone, facilitates continuous environmental monitoring and dynamic flight adjustment. This module continuously provides real-time distance data to the collision avoidance algorithm. Additionally, the system receives user control signals from the remote operator, which are then transmitted to the flight controller. Upon detecting an obstacle, the system immediately modifies these control signals to adjust the drone’s motion, thereby avoiding collisions and ensuring the safety of both the drone and its surroundings. The methodology involves initializing and calibrating multiple sensors, collecting and processing ranging data, and dynamically adjusting motion commands based on proximity alerts. This approach significantly improves the safety and operational efficiency of drones in complex indoor environments. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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27 pages, 1734 KB

Open AccessArticle

A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations

by Xiaomei Zhang, Xinchen Dai, Ping Lou and Jianmin Hu

Appl. Sci. 2025, 15(9), 4995; https://doi.org/10.3390/app15094995 - 30 Apr 2025

Cited by 8 | Viewed by 3492

Abstract

With the rapid development of e-commerce, the importance of logistics distribution is becoming increasingly prominent. In particular, the last-mile delivery is particularly important because it serves customers directly. Improving customer satisfaction is one of the important factors to ensure the quality of service in delivery and also an important guarantee for improving the market competitiveness of logistics enterprises. In the process of last-mile delivery, flexible delivery locations and variable delivery times are effective means to improve customer satisfaction. Therefore, this paper introduces a Vehicle Routing Problem with flexible time windows and delivery locations, considering customer satisfaction (VRP-CS), which considers customer satisfaction by using prospect theory from two aspects: the flexibility of delivery time and delivery locations. This VRP-CS is formally modeled as a bi-objective optimization problem, which is an NP-hard problem. To solve this problem, a Multi-Strategy Adaptive Large Neighborhood Search (MSALNS) method is proposed. Operators guided by strategies such as backtracking and correlation are introduced to create different neighborhoods for ALNS, thereby enriching search diversity. In addition, an acceptance criterion inspired by simulated annealing is designed to balance exploration and exploitation, helping the algorithm avoid being trapped in local optima. Extensive numerical experiments on generated benchmark instances demonstrate the effectiveness of the VRP-CS model and the efficiency of the proposed MSALNS algorithm. The experiment results on the generated benchmark instances show that the total cost of the VRP-CS is reduced by an average of 14.22% when optional delivery locations are utilized compared to scenarios with single delivery locations. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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17 pages, 6398 KB

Open AccessArticle

Integrated Optimization of Emergency Evacuation Routing for Dam Failure-Induced Flooding: A Coupled Flood–Road Network Modeling Approach

by Gaoxiang An, Zhuo Wang, Meixian Qu and Shaohua Hu

Appl. Sci. 2025, 15(8), 4518; https://doi.org/10.3390/app15084518 - 19 Apr 2025

Cited by 5 | Viewed by 3422

Abstract

Floods resulting from dam failures are highly destructive, characterized by intense impact forces, widespread inundation, and rapid flow velocities, all of which pose significant threats to public safety and social stability in downstream regions. To improve evacuation efficiency during such emergencies, it is essential to study flood evacuation route planning. This study aimed to minimize evacuation time and reduce risks to personnel by considering the dynamic evolution of dam-break floods. Using aerial photography from an unmanned aerial vehicle, the downstream road network of a reservoir was mapped. A coupled flood–road network coupling model was then developed by integrating flood propagation data with road network information. This model optimized evacuation route planning by combining the dynamic evolution of flood hazards with real-time road network data. Based on this model, a flood evacuation route planning method was proposed using Dijkstra’s algorithm. This methodology was validated through a case study of the Shanmei Reservoir in Fujian, China. The results demonstrated that the maximum flood level reached 18.65 m near Xiatou Village, and the highest flow velocity was 22.18 m/s near the Shanmei Reservoir. Furthermore, evacuation plans were developed for eight affected locations downstream of the Shanmei Reservoir, with a total of 13 evacuation routes. These strategies and routes resulted in a significant reduction in evacuation time and minimized the risks to evacuees. The life-loss risk was minimized in the evacuation process, and all evacuees were able to reach safe locations. These findings confirmed that the proposed method, which integrated flood dynamics with road network information, ensured the safety and effectiveness of evacuation routes. This approach met the critical needs of emergency management by providing timely and secure evacuation paths in the event of dam failure. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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26 pages, 2528 KB

Open AccessArticle

Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes

by Xiaomei Zhang, Ziang Liu, Jialiang Zhang, Yuhang Zeng and Chuannian Fan

Appl. Sci. 2025, 15(6), 3035; https://doi.org/10.3390/app15063035 - 11 Mar 2025

Cited by 4 | Viewed by 1646

Abstract

With the increasingly busy transportation of cargo at container terminals (CTs), the requirements for terminal throughput and operational efficiency are constantly increasing. The operational efficiency and cost of CTs are closely related to the seamless docking of terminal facilities, especially the joint operation between berths and quay cranes (QCs). Therefore, a joint allocation problem of berths and QCs (BACASP) is presented in this paper and formalized as a mathematical model to minimize terminal operation costs and shipowner dissatisfaction. Given that BACASP is an NP-hard problem, an improved multi-objective cuckoo search (IMOCS) algorithm is proposed to solve this problem, in which an elite-guided tangent flight strategy is presented to speed up the convergence for making up the lack of random search direction of the traditional cuckoo search algorithm; and an information-enhanced abandonment strategy is put forward to increase the possibility of escaping from local optima. Numerical experimental results show the effectiveness of the proposed algorithm. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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17 pages, 12340 KB

Open AccessArticle

Autonomous Robot Goal Seeking and Collision Avoidance in the Physical World: An Automated Learning and Evaluation Framework Based on the PPO Method

by Wen-Chung Cheng, Zhen Ni, Xiangnan Zhong and Minghan Wei

Appl. Sci. 2024, 14(23), 11020; https://doi.org/10.3390/app142311020 - 27 Nov 2024

Cited by 10 | Viewed by 4115

Abstract

Mobile robot navigation is a critical aspect of robotics, with applications spanning from service robots to industrial automation. However, navigating in complex and dynamic environments poses many challenges, such as avoiding obstacles, making decisions in real-time, and adapting to new situations. Reinforcement Learning (RL) has emerged as a promising approach to enable robots to learn navigation policies from their interactions with the environment. However, application of RL methods to real-world tasks such as mobile robot navigation, and evaluating their performance under various training–testing settings has not been sufficiently researched. In this paper, we have designed an evaluation framework that investigates the RL algorithm’s generalization capability in regard to unseen scenarios in terms of learning convergence and success rates by transferring learned policies in simulation to physical environments. To achieve this, we designed a simulated environment in Gazebo for training the robot over a high number of episodes. The training environment closely mimics the typical indoor scenarios that a mobile robot can encounter, replicating real-world challenges. For evaluation, we designed physical environments with and without unforeseen indoor scenarios. This evaluation framework outputs statistical metrics, which we then use to conduct an extensive study on a deep RL method, namely the proximal policy optimization (PPO). The results provide valuable insights into the strengths and limitations of the method for mobile robot navigation. Our experiments demonstrate that the trained model from simulations can be deployed to the previously unseen physical world with a success rate of over

88 %

. The insights gained from our study can assist practitioners and researchers in selecting suitable RL approaches and training–testing settings for their specific robotic navigation tasks. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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11 pages, 1668 KB

Open AccessArticle

Development of Traffic Scheduling Based on TSN in Smart Substation Devices

by Xin Mei, Jin Wang, Chang Liu, Chang Liu, Jiangpei Xu, Zishang Cui, Lijun Peng and Bing Chen

Appl. Sci. 2024, 14(22), 10135; https://doi.org/10.3390/app142210135 - 5 Nov 2024

Cited by 2 | Viewed by 2992

Abstract

Smart substations are an important trend in substation construction. With increasing data traffic, it is difficult for the traditional Ethernet network to meet the real-time requirements of control information in smart substations. Hence, in this paper, a deterministic network architecture for substations based on time-sensitive networks (TSN) has been developed in order to realize the domain-wide time synchronization and efficient real-time communication of the “three-layer and two-network” model in smart substations. Furthermore, a design scheme for substation automation equipment based on TSN is proposed. The proposed device realizes the timely transmission of real-time control information packets by utilizing the Earliest TxTime First (ETF) Qdisc technology of Linux and the timing sending capability of Intel 210 NIC. Furthermore, it collaborates with the time-aware shaper (TAS) traffic scheduling mechanism of TSN switches to ensure the end-to-end deterministic delay of time-sensitive traffic. As a result, it provides efficient real-time communication services with low latency and jitter for smart substation automation systems. Full article

(This article belongs to the Special Issue AI-Based Methods for Object Detection and Path Planning)

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