Intelligent Vision Research Articles

Intelligent Vision System with Pruning and Web Interface for Real-Time Defect Detection on African Plum Surfaces

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at a large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG-16, DenseNet-121, MobileNet, and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models, respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10–30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap.

Multifunctional human visual pathway-replicated hardware based on 2D materials

Artificial visual system empowered by 2D materials-based hardware simulates the functionalities of the human visual system, leading the forefront of artificial intelligence vision. However, retina-mimicked hardware that has not yet fully emulated the neural circuits of visual pathways is restricted from realizing more complex and special functions. In this work, we proposed a human visual pathway-replicated hardware that consists of crossbar arrays with split floating gate 2D tungsten diselenide (WSe2) unit devices that simulate the retina and visual cortex, and related connective peripheral circuits that replicate connectomics between the retina and visual cortex. This hardware experimentally displays advanced multi-functions of red–green color-blindness processing, low-power shape recognition, and self-driven motion tracking, promoting the development of machine vision, driverless technology, brain–computer interfaces, and intelligent robotics.

생성형 인공지능 기술이 출판 콘텐츠 생산 과정 변화에 미치는 영향 연구

Artificial intelligence technology is being applied to the publishing industry in various forms. This study focused on content among the C-P-N-D perspectives that use artificial intelligence technology that is affecting the publishing ecosystem as a media ecosystem analysis framework. As a result of the study, visual intelligence technology was being applied to image generation areas such as book covers and book design generation of publications. Language intelligence technology was being applied to content planning, automation content generation, content editing and revision areas in connection with text generation. The expert system used artificial intelligence technology in the publishing process to optimize various areas such as content planning, creation, editing, and marketing, and bring about changes in the efficiency of work and reduction of production cost. This study discussed changes and major issues, which were grafted in the publishing area and artificial intelligence technology focused on content (C). Understanding the environmental changes and applications that artificial intelligence technology will bring to the publishing industry can strengthen the understanding and direction of the publishing industry.

Coupled Impact of Points of Interest and Thermal Environment on Outdoor Human Behavior Using Visual Intelligence

Although it is well established that thermal environments significantly influence travel behavior, the synergistic effects of points of interest (POI) and thermal environments on behavior remain unclear. This study developed a vision-based outdoor evaluation model aimed at uncovering the driving factors behind human behavior in outdoor spaces. First, Yolo v5 and questionnaires were employed to obtain crowd activity intensity and preference levels. Subsequently, target detection and clustering algorithms were used to derive variables such as POI attractiveness and POI distance, while a validated environmental simulator was utilized to simulate outdoor thermal comfort distributions across different times. Finally, multiple classification models were compared to establish the mapping relationships between POI, thermal environment variables, and crowd preferences, with SHAP analysis used to examine the contribution of each variable. The results indicate that XGBoost achieved the best predictive performance (accuracy = 0.95), with shadow proportion (|SHAP| = 0.24) and POI distance (|SHAP| = 0.12) identified as the most significant factors influencing crowd preferences. By extrapolation, this classification model can provide valuable insights for optimizing community environments and enhancing vitality in areas with similar climatic and cultural contexts.

Combining visual intelligence and social-physical urban features facilitates fine-scale seasonality characterization of urban thermal environments

Traditional methods for assessing urban thermal environments (UTEs) often rely on GIS and remote sensing data, suffering from data limitations in coverage, accuracy, and availability. To address these challenges, we established a novel framework developing visual intelligence simulated indices (VISIs) for improved UTE characterization. This framework combines GIS spatial data and pre-trained large-scale and local-trained GeoAI models to generate visual intelligence, extracting GIS-like visual elements from remote sensing imagery. These GIS-like VISIs comprehensively represent land surface temperature (LST) and urban fabric for cost-effective UTE analysis. We applied our framework to Zhengzhou, China, a city with diverse landscapes. Comparative experiments among Random Forests (RF), Neural Networks (NN), and Convolutional Neural Networks (CNN) demonstrated that RF models exhibited superior performance in LST representation (0.7 test R2 and 0.9 training R2). Interestingly, CNNs performed better than RFs and NNs when less cloud cover (<6 %) was present, suggesting that data diversity is essential for CNN to learn generalizable features for LST representation. For urban fabric representation, feature importance analysis indicated that VISIs (37 %) derived from large-scale GeoAI models outperform spectral bands (20 %). Furthermore, both Pearson correlation and Shapley values showed that VISIs (e.g., building features) better characterize urban heat islands than traditional remote sensing ecological indices (RSEIs) such as NDBI, underscoring the cost-effectiveness of our visual intelligence approach. In summary, our visual intelligence method effectively consolidates UTE seasonality characterization into a predictable scenario even given the unpredictable availability of remote sensing and GIS data, revealing novel perspectives on sensing unseen details for UTEs.

Maximum entropy intrinsic learning for spiking networks towards embodied neuromorphic vision

Spiking neural network (SNN), as a brain-inspired model, possesses outstanding low power consumption and the ability to mimic biological neuron mechanisms. Embodied vision is a promising field, which requires the low-power advantage of SNNs. However, SNN faces difficulties in achieving real-time, high generalization ability, and robustness in the implementation of embodied vision due to the limitations of existing training methods. In this paper, to prevent model overfitting to noise and unknown environment in embodied neuromorphic visual intelligence, we present a new and efficient learning strategy designed to enhance the training performance of deep SNNs, called Spiking Maximum Entropy Intrinsic Learning (SMEIL). The learning algorithm essentially promotes the perturbation of the underlying source distribution, which in turn enlarges the predictive uncertainty of the current model. This approach enhances the model's robustness and improves its ability to generalize during the training process. Superior performance across a variety of data sets is achieved, and different types of noise are added to SMEIL algorithm for testing its robustness. Experiments show that SMEIL can consistently improve the learning robustness over each noise disturbance, and can cut down the power consumption during training significantly. Hence, it is a powerful method for advancing direct training of deep SNNs, and opens a novel point of view for developing novel spike-based learning algorithm towards embodied neuromorphic intelligence.

Development of Safety Monitoring System Based on Visual Intelligence Technology

Development of Safety Monitoring System Based on Visual Intelligence Technology

The interplay of learning, analytics and artificial intelligence in education: A vision for hybrid intelligence

The interplay of learning, analytics and artificial intelligence in education: A vision for hybrid intelligence

Real-time visual intelligence for defect detection in pharmaceutical packaging

Defect detection in pharmaceutical blister packages is the most challenging task to get an accurate result in detecting defects that arise in tablets while manufacturing. Conventional defect detection methods include human intervention to check the quality of tablets within the blister packages, which is inefficient, time-consuming, and increases labor costs. To mitigate this issue, the YOLO family is primarily used in many industries for real-time defect detection in continuous production. To enhance the feature extraction capability and reduce the computational overhead in a real-time environment, the CBS-YOLOv8 is proposed by enhancing the YOLOv8 model. In the proposed CBS-YOLOv8, coordinate attention is introduced to improve the feature extraction capability by capturing the spatial and cross-channel information and also maintaining the long-range dependencies. The BiFPN (weighted bi-directional feature pyramid network) is also introduced in YOLOv8 to enhance the feature fusion at each convolution layer to avoid more precise information loss. The model's efficiency is enhanced through the implementation of SimSPPF (simple spatial pyramid pooling fast), which reduces computational demands and model complexity, resulting in improved speed. A custom dataset containing defective tablet images is used to train the proposed model. The performance of the CBS-YOLOv8 model is then evaluated by comparing it with various other models. Experimental results on the custom dataset reveal that the CBS-YOLOv8 model achieves a mAP of 97.4% and an inference speed of 79.25 FPS, outperforming other models. The proposed model is also evaluated on SESOVERA-ST saline bottle fill level monitoring dataset achieved the mAP50 of 99.3%. This demonstrates that CBS-YOLOv8 provides an optimized inspection process, enabling prompt detection and correction of defects, thus bolstering quality assurance practices in manufacturing settings.

Practical Application of Intelligent Vision Measurement System Based on Deep Learning

To comprehensively assess the true visual function of clinical dry eye patients and the comprehensive impact of blinking characteristics on functional vision of the human eye, an intelligent vision measurement system has been designed and developed to detect and analyze blinks from the side. The system employs deep learning keypoint recognition technology to analyze eyelid features from a lateral perspective. It presents the data of identified key points for the upper and lower eyelids in a line chart format and annotates the trough of each blink. By setting benchmark values, the system automatically calculates the proportion of complete and incomplete blinks in the tested individuals. The results indicate that the system is stable in performance and accurate in measurement, successfully achieving the anticipated design objectives. It thereby provides reliable technical support for future clinical applications.

Turbulent Image Restoration in Atmosphere with Cyclopean Processing via Binocular Fusion

Abstract The outstanding issue to overcoming atmospheric turbulence on distant imaging is a fundamental interest and technological challenge. We propose a novel scenario and technique to restore the optical image in turbulent environmental by referring to Cyclopean image with binocular vision. With human visual intelligence, image distortion resulting from the turbulence is shown to be substantially suppressed. Numerical simulation results taking into account of the atmospheric turbulence, optical image system, image sensors, display and binocular vision perception are presented to demonstrate the robustness of the image restoration, which is compared with a single channel planar optical imaging and sensing. Experiment involving binocular telescope, image recording and the stereo-image display is conducted and good agreement is obtained between the simulation with perceptive experience. A natural extension of the scenario is to enhance the capability of anti-vibration or anti-shaking for general optical imaging with Cyclopean image.

Puppet Dynasty Recognition System Based on MobileNetV2.

Traditional image classification usually relies on manual feature extraction; however, with the rapid development of artificial intelligence and intelligent vision technology, deep learning models such as CNNs can automatically extract key features from input images to achieve efficient classification. This study focuses on the application of lightweight separable convolutional neural networks in domain-specific image classification tasks. In this paper, we discuss how to use the SSDLite object detection algorithm combined with the MobileNetV2 lightweight convolutional architecture for puppet dynasty recognition from images-a novel and challenging task. By constructing a system that combines object detection and image classification, we aimed to solve the problem of automatic puppet dynasty recognition to reduce manual intervention and improve recognition efficiency and accuracy. We hope that this will have significant implications in the fields of cultural protection and art history research.

Visionary vigilance: Optimized YOLOV8 for fallen person detection with large-scale benchmark dataset

Falls pose a significant risk to elderly people, patients with diseases such as neurological disorders, cardiovascular diseases, and disabled children. This highlights the need for real-time intelligent fall detection (FD) systems for quick relief leading to assisted living. The existing attempts are often based on multimodal approaches which are computationally expensive due to multi-sensor integration. The computer vision (CV) based era for FD needs the deployment of state-of-the-art (SOTA) networks with progressive enhancements to grasp falls effectively. However, CV-based systems often lack the ability to operate efficiently in real-time and the attempts for visual intelligence are usually not integrated at feasible stages of the networks. More importantly, the lack of large-scale well-annotated benchmark datasets limits the ability of FD in challenging and complex environments. To bridge the research gaps, we proposed an enhanced version of YOLOV8 for FD. Our research presents significant contributions by addressing these limitations through three key contributions. Initially, a comprehensive large-scale dataset is introduced which comprises approximately 10,500 image samples with corresponding annotations. The dataset encompasses diverse environmental conditions and scenarios, facilitating the generalization ability for the models. Then, progressive enhancements to the YOLOV8S model are proposed, integrating a focus module in the backbone to optimize feature extraction. Moreover, the convolutional block attention modules (CBAMs) are integrated at the feasible stages of the network to improve spatial and channel contexts for more accurate detection, especially in complex scenes. Finally, an extensive empirical evaluation showcases the superiority of the proposed network over 13 SOTA techniques, substantiated by meticulous benchmarking and qualitative validation across varied environments. The empirical findings and analysis of multiple factors such as model performance, size, and processing time prove that the suggested network displays impressive results. Datasets with annotations, results, and the ways of progressive modifications in the code will be available to the research community at the link https://github.com/habib1402/Fall-Detection-DiverseFall10500

Artificial intelligence vision technology application in sustainability evaluation of solar-driven distillation device

The process of transforming brackish water into freshwater utilizing solar thermal energy is referred to as solar-driven distillation device, or solar still. Such device without fossil fuel provides significant environmental and health benefits by reducing air pollutants and remedying brackish water. The yield of purified water from conventional solar stills remains insufficient, prompting the necessity for research to enhance it by understanding the coupling effect between steam flow, heat transfer, and mass transfer. As such, computer-aided brackish water treatment comparison of the hemispherical solar still and other multi-slope solar stills is conducted in this paper, and an automatic steam&heat flow detection algorithm is developed to solve the problem of difficult data acquisition for gas-liquid transport processes. Firstly, double slope, four slope, and hemispherical solar stills are exposed to the sun in outdoor experiments, therefore the solar thermal performance of each still is analyzed through pairwise comparisons. Secondly, a large amount of experimental image data is input into neural network for training, and by continuously adjusting network parameters, the network can accurately recognize different types of images. Finally, the image to be recognized is input into the trained neural network, which outputs the category labels of the image to achieve automatic image recognition. Based on the data above, the best structure of solar-driven device is hemispherical structure, due to that the hemispherical solar still possesses the best performance in terms of distilled water yield, energy efficiency, exergy efficiency and energy payback.

Internalizing Lego-lego of Buginese People into Project Based Learning Model

The purpose of this study is to reveal the concept of lego-lego in the project-based learning model. This study is a literature that uses a conceptual approach and focuses on the combination of the Project Based Learning model with intrinsic values in society, especially in South Sulawesi. Types and sources of data are obtained from primary data in the form of documents related to learning models based on Project Based Learning. Lego-lego can provide learning values so as to redevelop various skills and visual intelligence, creativity, and character of learners in completing their tasks

Design of intelligent controller for obstacle avoidance and navigation of electric patrol mobile robot based on PLC

Currently, the obstacle avoidance control of patrol robots based on intelligent vision lacks professional controller module assistance. Therefore, this paper proposes a design method of intelligent controller for obstacle avoidance and navigation of electrical inspection mobile robot based on PLC control. The controller designs a laser range finder to determine the required position of electrical patrol inspection. Use PLC as the core controller, and combine sensors, actuators, communication module and PLC selection module in the process of hardware design to achieve autonomous navigation and obstacle avoidance functions of the robot. Then design the software including the PLC compiler system and the virtual machine module. Based on the above steps, design the control module of obstacle avoidance navigation, which realizes the key link of robot autonomous navigation. The test results show that the controller can successfully avoid obstacles, improve the efficiency and quality of inspection, and achieve accurate and fast obstacle avoidance navigation for the electrical inspection mobile robot.

Freak Pictures, Fly Fishers, and Optical Illusions: Historical Antecedents of the Science of Fish Vision

Abstract In the early twentieth century, when little was known about the underwater realm, the question of subaquatic vision garnered much interest. Based on human laws of optics in water, observers of the time claimed to show how fish see, and the impact of illusions on fish vision. Addressing fly-fishers in particular, they raised the question of whether fish see “freak pictures” and “monsters.” The present study examines five scientific visualizations published in Scientific American in 1913 to determine whether optical illusions remain relevant for fish scientists today and whether conceptions of visual intelligence in fish have changed since 1913.

Effective Video Summarization Using Channel Attention-Assisted Encoder–Decoder Framework

A significant number of cameras regularly generate massive amounts of data, demanding hardware, time, and labor resources to acquire, process, and monitor. Asymmetric frames within videos pose a challenge to automatic summarization of videos, making it challenging to capture key content. Developments in computer vision have accelerated the seamless capture and analysis of high-resolution video content. Video summarization (VS) has garnered considerable interest due to its ability to provide concise summaries of lengthy videos. The current literature mainly relies on a reduced set of representative features implemented using shallow sequential networks. Therefore, this work utilizes an optimal feature-assisted visual intelligence framework for representative feature selection and summarization. Initially, the empirical analysis of several features is performed, and ultimately, we adopt a fine-tuning InceptionV3 backbone for feature extraction, deviating from conventional approaches. Secondly, our strategic encoder–decoder module captures complex relationships with five convolutional blocks and two convolution transpose blocks. Thirdly, we introduced a channel attention mechanism, illuminating interrelations between channels and prioritizing essential patterns to grasp complex refinement features for final summary generation. Additionally, comprehensive experiments and ablation studies validate our framework's exceptional performance, consistently surpassing state-of-the-art networks on two benchmarks (TVSum and SumMe) datasets.

Toward Intraoperative Visual Intelligence: Real-Time Surgical Instrument Segmentation for Enhanced Surgical Monitoring.

Open surgery relies heavily on the surgeon's visual acuity and spatial awareness to track instruments within a dynamic and often cluttered surgical field. This system utilizes a head-mounted depth camera to monitor surgical scenes, providing both image data and depth information. The video captured from this camera is scaled down, compressed using MPEG, and transmitted to a high-performance workstation via the RTSP (Real-Time Streaming Protocol), a reliable protocol designed for real-time media transmission. To segment surgical instruments, we utilize the enhanced U-Net with GridMask (EUGNet) for its proven effectiveness in surgical tool segmentation. For rigorous validation, the system's performance reliability and accuracy are evaluated using prerecorded RGB-D surgical videos. This work demonstrates the potential of this system to improve situational awareness, surgical efficiency, and generate data-driven insights within the operating room. In a simulated surgical environment, the system achieves a high accuracy of 85.5% in identifying and segmenting surgical instruments. Furthermore, the wireless video transmission proves reliable with a latency of 200 ms, suitable for real-time processing. These findings represent a promising step towards the development of assistive technologies with the potential to significantly enhance surgical practice.

Optifusion: advancing visual intelligence in medical imaging through optimized CNN-TQWT fusion

Optifusion: advancing visual intelligence in medical imaging through optimized CNN-TQWT fusion