Abstract Fires in smart cities can have devastating consequences, causing damage to property, and endangering the lives of citizens. Traditional fire detection methods have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. This paper proposes an improved fire detection approach for smart cities based on the YOLOv8 algorithm, called the smart fire detection system (SFDS), which leverages the strengths of deep learning to detect fire-specific features in real time. The SFDS approach has the potential to improve the accuracy of fire detection, reduce false alarms, and be cost-effective compared to traditional fire detection methods. It can also be extended to detect other objects of interest in smart cities, such as gas leaks or flooding. The proposed framework for a smart city consists of four primary layers: (i) Application layer, (ii) Fog layer, (iii) Cloud layer, and (iv) IoT layer. The proposed algorithm utilizes Fog and Cloud computing, along with the IoT layer, to collect and process data in real time, enabling faster response times and reducing the risk of damage to property and human life. The SFDS achieved state-of-the-art performance in terms of both precision and recall, with a high precision rate of 97.1% for all classes. The proposed approach has several potential applications, including fire safety management in public areas, forest fire monitoring, and intelligent security systems. 

An improved fire detection approach based on YOLO-v8 for smart cities

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

A solar cell defect detection method with an improved YOLO v5 algorithm is proposed for the characteristics of the complex solar cell image background, variable defect morphology, and large-scale differences. First, the deformable convolution is incorporated into the CSP module to achieve an adaptive learning scale and perceptual field size; then, the feature extraction capability of the model is enhanced by introducing the ECA-Net attention mechanism; finally, the model network structure is improved and one tiny defect prediction head is added to improve the accuracy of target detection at different scales. To further optimize and improve the YOLO v5 algorithm, this paper uses Mosaic and MixUp fusion data enhancement, K-means++ clustering anchor box algorithm, and CIOU loss function to enhance the model performance. The experimental results show that the improved YOLO v5 algorithm achieves 89.64% mAP for the model trained on the solar cell EL image dataset, which is 7.85% higher than the mAP of the original algorithm, and the speed reaches 36.24 FPS, which can complete the solar cell defect detection task more accurately while meeting the real-time requirements.

/pdf/solar-cell-surface-defect-detection-based-on-improved-yolo-2fxo3kaq.pdf

Solar Cell Surface Defect Detection Based on Improved YOLO v5

Forest fires are a serious ecological concern, and smoke is an early warning indicator. Early smoke images barely capture a tiny portion of the total smoke. Because of the irregular nature of smoke’s dispersion and the dynamic nature of the surrounding environment, smoke identification is complicated by minor pixel-based traits. This study presents a new framework that decreases the sensitivity of various YOLO detection models. Additionally, we compare the detection performance and speed of different YOLO models such as YOLOv3, YOLOv5, and YOLOv7 with prior ones such as Fast R-CNN and Faster R-CNN. Moreover, we follow the use of a collected dataset that describes three distinct detection areas, namely close, medium, and far distance, to identify the detection model’s ability to recognize smoke targets correctly. Our model outperforms the gold-standard detection method on a multi-oriented dataset for detecting forest smoke by an mAP accuracy of 96.8% at an IoU of 0.5 using YOLOv5x. Additionally, the findings of the study show an extensive improvement in detection accuracy using several data-augmentation techniques. Moreover, YOLOv7 outperforms YOLOv3 with an mAP accuracy of 95%, compared to 94.8% using an SGD optimizer. Extensive research shows that the suggested method achieves significantly better results than the most advanced object-detection algorithms when used on smoke datasets from wildfires, while maintaining a satisfactory performance level in challenging environmental conditions.

/pdf/early-wildfire-smoke-detection-using-different-yolo-models-1lt6v2w0.pdf

Early Wildfire Smoke Detection Using Different YOLO Models

Polyps are very common abnormalities in human gastrointestinal regions. Their early diagnosis may help in reducing the risk of colorectal cancer. Vision-based computer-aided diagnostic systems automatically identify polyp regions to assist surgeons in their removal. Due to their varying shape, color, size, texture, and unclear boundaries, polyp segmentation in images is a challenging problem. Existing deep learning segmentation models mostly rely on convolutional neural networks that have certain limitations in learning the diversity in visual patterns at different spatial locations. Further, they fail to capture inter-feature dependencies. Vision transformer models have also been deployed for polyp segmentation due to their powerful global feature extraction capabilities. But they too are supplemented by convolution layers for learning contextual local information. In the present paper, a polyp segmentation model CoInNet is proposed with a novel feature extraction mechanism that leverages the strengths of convolution and involution operations and learns to highlight polyp regions in images by considering the relationship between different feature maps through a statistical feature attention unit. To further aid the network in learning polyp boundaries, an anomaly boundary approximation module is introduced that uses recursively fed feature fusion to refine segmentation results. It is indeed remarkable that even tiny-sized polyps with only 0.01% of an image area can be precisely segmented by CoInNet. It is crucial for clinical applications, as small polyps can be easily overlooked even in the manual examination due to the voluminous size of wireless capsule endoscopy videos. CoInNet outperforms thirteen state-of-the-art methods on five benchmark polyp segmentation datasets. 

CoInNet: A Convolution-Involution Network With a Novel Statistical Attention for Automatic Polyp Segmentation

Fast and accurate smoke detection is very important for reducing fire damage. Due to the complexity and changeable nature of smoke scenes, existing smoke detection technology has the problems of a low detection rate and a high false negative rate, and the robustness and generalization ability of the algorithms are not high. Therefore, this paper proposes a smoke detection model based on the improved YOLOv5. First, a large number of real smoke and synthetic smoke images were collected to form a dataset. Different loss functions (GIoU, DIoU, CIoU) were used on three different models of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l), and YOLOv5m was used as the baseline model. Then, because of the problem of small numbers of smoke training samples, the mosaic enhancement method was used to randomly crop, scale and arrange nine images to form new images. To solve the problem of inaccurate anchor box prior information in YOLOv5, a dynamic anchor box mechanism is proposed. An anchor box was generated for the training dataset through the k-means++ clustering algorithm. The dynamic anchor box module was added to the model, and the size and position of the anchor box were dynamically updated in the network training process. Aiming at the problem of unbalanced feature maps in different scales of YOLOv5, an attention mechanism is proposed to improve the network detection performance by adding channel attention and spatial attention to the original network structure. Compared with the traditional deep learning algorithm, the detection performance of the improved algorithm in this paper was is 4.4% higher than the mAP of the baseline model, and the detection speed reached 85 FPS, which is obviously better and can meet engineering application requirements.

/pdf/a-smoke-detection-model-based-on-improved-yolov5-3fvrd2gf.pdf

A Smoke Detection Model Based on Improved YOLOv5

In this paper, a fine-to-finer segmentation task is investigated driven by region and contour features collaboratively on Glomerular Electron-Dense Deposits (GEDD) in view of the complementary nature of these two types of features. To this end, a novel network (Co-Net) is presented to dynamically use fine saliency segmentation to guide finer segmentation on boundaries. The whole architecture contains double mutually boosted decoders sharing one common encoder. Specifically, a new structure named Global-guided Interaction Module (GIM) is designed to effectively control the information flow and reduce redundancy in the cross-level feature fusion process. At the same time, the global features are used in it to make the features of each layer gain access to richer context, and a fine segmentation map is obtained initially; Discontinuous Boundary Supervision (DBS) strategy is applied to pay more attention to discontinuity positions and modifying segmentation errors on boundaries. At last, Selective Kernel (SK) is used for dynamical aggregation of the region and contour features to obtain a finer segmentation. Our proposed approach is evaluated on an independent GEDD dataset labeled by pathologists and also on open polyp datasets to test the generalization. Ablation studies show the effectiveness of different modules. On all datasets, our proposal achieves high segmentation accuracy and surpasses previous methods.

Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation

Since the results of CNN methods for monocular depth estimation generally suffer the problem of visual dissatisfaction, we propose Feature Fusion GAN (FF-GAN) to address this issue. First, an end-to-end network based on encoder-decoder structure is proposed as the generator of FF-GAN, which can exploit the information of different scales. The encoder of our generator fuse features in different levels with a feature fusion module. The component which can obtain the information of multi-scale receptive field is the main part of the decoder of our generator. Second, in order to match the generator, the discriminator of FF-GAN is designed to efficiently learn the information of different scales by applying pyramid structure. Experiments on public datasets demonstrate the effectiveness of our generator and discriminator. Compared with the CNN methods, the results predicted by FF-GAN are significantly improved in terms of texture loss and edge blur while ensuring accuracy, and the visual effect is better.

FF-GAN: Feature Fusion GAN for Monocular Depth Estimation

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A Smoke Detection Model Based on Improved YOLOv5

Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation

FF-GAN: Feature Fusion GAN for Monocular Depth Estimation