A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments

The precise identification of disease traits in the complex sugarcane planting environment not only effectively prevents the spread and outbreak of common diseases but also allows for the real-time monitoring of nutrient deficiency syndrome at the top of sugarcane, facilitating the supplementation of relevant nutrients to ensure sugarcane quality and yield. This paper proposes a human–machine collaborative sugarcane disease detection method in complex environments. Initially, data on five common sugarcane diseases—brown stripe, rust, ring spot, brown spot, and red rot—as well as two nutrient deficiency conditions—sulfur deficiency and phosphorus deficiency—were collected, totaling 11,364 images and 10 high-definition videos captured by a 4K drone. The data sets were augmented threefold using techniques such as flipping and gamma adjustment to construct a disease data set. Building upon the YOLOv8 framework, the EMA attention mechanism and Focal loss function were added to optimize the model, addressing the complex backgrounds and imbalanced positive and negative samples present in the sugarcane data set. Disease detection models EF-yolov8s, EF-yolov8m, EF-yolov8n, EF-yolov7, and EF-yolov5n were constructed and compared. Subsequently, five basic instance segmentation models of YOLOv8 were used for comparative analysis, validated using nutrient deficiency condition videos, and a human–machine integrated detection model for nutrient deficiency symptoms at the top of sugarcane was constructed. The experimental results demonstrate that our improved EF-yolov8s model outperforms other models, achieving mAP_0.5, precision, recall, and F1 scores of 89.70%, 88.70%, 86.00%, and 88.00%, respectively, highlighting the effectiveness of EF-yolov8s for sugarcane disease detection. Additionally, yolov8s-seg achieves an average precision of 80.30% with a smaller number of parameters, outperforming other models by 5.2%, 1.9%, 2.02%, and 0.92% in terms of mAP_0.5, respectively, effectively detecting nutrient deficiency symptoms and addressing the challenges of sugarcane growth monitoring and disease detection in complex environments using computer vision technology.

Currently, the forest fires are worldwide problem which requires a complete solution. The consequences of it are related, not only, to the environmental and biodiversity destruction but also to human and economic losses. In this article, forest fire and smoke detection systems, methods and technics literature review are carried out. To accomplish this aim, a generic mobile and non-mobile monitoring and surveillance systems applied in this field analysis took place. From the review carried out, it has been found that for fire detection two types of methods have been used, one based on machine learning and another through color features extraction of images or video frames, on the other hand, the combination of both methods is used for the detection of smoke and fire or only smoke, this guarantees accurate rates of more than 90%. Finally, a detailed analysis of the use of these fire and smoke detection algorithms in the UAS/UAV’s systems was carried out paying special attention on them due to the flexibility, versatility, and maneuverability abilities among others displayed by these systems that help prevent and mitigate forest fires.KeywordsFire forestSmoke forestMachine learningColor features extraction

A single-chip CMOS smoke and temperature sensor for use as an intelligent fire detector is proposed. The proposed smoke sensor measures smoke density based on the light-scattering method. The temperature sensor is integrated with the smoke sensor not only to sense heat from a fire but also to compensate for the temperature dependency of the smoke sensor. The prototype chip includes an on-chip photodiode (PD), pixel circuit, correlated double sampling (CDS) circuit, and analog-to-digital converter (ADC). The prototype chip was fabricated using a 0.35-mu m CMOS process and was placed inside the smoke detection chamber, while the thermistor for the temperature sensor is placed outside the chamber. The measurement results show plusmn 1% smoke detection accuracy over the range 4% ~ 25% and plusmn1degC temperature-sensing accuracy over the range 25degC ~ 95degC. The power consumption of the prototype chip is 220 nW, excluding the infrared light-emitting diode (IR LED).

Efficient attention based deep fusion CNN for smoke detection in fog environment

Prohibited item detection is crucial for the safety of public places. Deep learning, one of the mainstream methods in prohibited item detection tasks, has shown superior performance far beyond traditional prohibited item detection methods. However, most neural network architectures in deep learning still lack sufficient local feature representation ability for overlapping and small targets, and ignore the problem of semantic conflicts caused by direct feature fusion. In this paper, we propose MFPIDet, a novel prohibited item detection neural network architecture based on improved YOLOV7 to achieve reliable prohibited item detection in complex environments. Specifically, a multi-scale attention module (MAM) backbone is proposed to filter the redundant information of target regions and further applied to enhance the local feature representation ability of overlapping objects. Here, to reduce the redundant information of target regions, a squeeze-excitation (SE) block is used to filter the background. Then, aiming at enhancing the feature expression ability of overlapping objects, a multi-scale feature extraction module (MFEM) is designed for local feature representation. In addition, to obtain richer context information, We design an adaptive fusion feature pyramid network (AF-FPN) to combine the adaptive context information fusion module (ACIFM) with the feature fusion module (FFM) to improve the neck structure of YOLOV7. The proposed method is validated on the PIDray dataset, and the tested results showed that our method obtained the highest mAP (68.7%), which is improved by 3.5% than YOLOV7 methods. Our approach provides a new design pattern for prohibited item detection in complex environments and shows the development potential of deep learning in related fields.

Traditional flame and smoke detection mostly rely on temperature and smoke sensor, but the detection of temperature detector and smoke detector has a certain lag. In order to solve this problem of hysteresis and low accuracy, we propose an improved YOLOV3 algorithm combined with attention mechanism for flame and smoke detection. Firstly, a multi-scene large-scale flame and smoke image dataset is built. The localization and classification of the flame and smoke areas in the image are annotated precisely. The suspected areas of the flame and smoke in the image are obtained by color analysis, so that the suspected areas of the flame and smoke objects are concerned. Then combined with the feature extraction ability of deep network, the problem of flame and smoke detection is transformed into multi-classification and coordinate regression. Finally, the detection model of flame and smoke in multi-scene is obtained. Our experiments show the effectiveness of the improved YOLOv3 algorithm combined with attention mechanism in flame and smoke detection. Our proposed method achieves outstanding performance in the dataset of flame and smoke image. The detection speed also meets the need of real-time detection.KeywordsFlame and smoke detectionAttention mechanismsYOLOv3Deep learning

In dynamic and complex real-world environments, artificial intelligence (AI) vision systems continue to face significant challenges in accurately detecting and tracking small objects. The core difficulty lies in the fact that small targets usually exhibit limited spatial and textural features, while dynamic backgrounds often generate numerous misleading motion cues, thereby interfering with reliable discrimination between targets and backgrounds. Inspired by the remarkable capability of the insect brain in detecting small moving objects, this study proposes a visual neural network model enhanced by a feedback mechanism. By adaptively responding to temporal variations, the proposed model is able to more precisely distinguish small targets from background-induced false targets. The network architecture consists of two main pathways: a motion detection pathway that extracts motion-related features from minute targets, and a feedback attention pathway that enhances the focus on true targets by leveraging the feature differences between real and false motion signals. In addition, a global inhibition module is incorporated to reduce the false alarm rate by filtering out background-induced false positives, thereby improving the overall detection performance of the model. Experimental results demonstrate that the proposed model achieves a detection rate of 0.81 in complex visual scenarios, whereas the compared models all achieve detection rates below 0.59, indicating a significant improvement in detection performance. Meanwhile, in terms of Precision and F1-score, the proposed model achieves values of 0.0648 and 0.12, respectively, while the compared models obtain values lower than 0.0077 and 0.015, further validating the superiority of the proposed method in detection accuracy and robustness.

One of the fundamental operations in computer vision applications is change detection, in which moving foreground objects are segmented from a static background. A common approach for change detection is the comparison of an image frame with the stored background model using a matching algorithm, a process known as background subtraction. However, such techniques fail in environments with dynamic backgrounds, illumination changes, or shadow and camera jitters. This study focuses on effectively detecting scene changes in complex environments. To this end, we proposed a new colour descriptor named local colour difference pattern (LCDP) that is insusceptible to shadow and is able to capture both colour and texture features at a pixel location. Furthermore, a scene change detection framework was proposed to handle dynamic scenes based on sample consensus that integrates LCDP and a novel spatial model fusion mechanism. Experiments using the CDnet benchmark dataset demonstrated the effectiveness of the proposed approach to change detection in complex environments.

Aiming at the core problem of high false and missed alarm rate and insufficient interference resistance of existing smoke and fire detection algorithms in complex scenes, this paper proposes a target detection network based on improved feature pyramid structure. By constructing a Context Guided Convolutional Block instead of the traditional convolutional operation, the detected target and the surrounding environment information are fused with secondary features while reconfiguring the feature dimensions, which effectively solves the problem of edge feature loss in the down-sampling process. The Poly Kernel Inception Block is designed, and a multi-branch parallel network structure is adopted to realize multi-scale feature extraction of the detected target, and the collaborative characterization of the flame profile and smoke diffusion pattern is realized. In order to further enhance the logical location sensing ability of the target, a Manhattan Attention Mechanism Unit is introduced to accurately capture the spatial and temporal correlation characteristics of the flame and smoke by establishing a pixel-level long-range dependency model. Experimental tests are conducted using a self-constructed high-quality smoke and fire image dataset, and the results show that, compared with the existing typical lightweight smoke and fire detection models, the present algorithm has a significant advantage in detection accuracy, and it can satisfy the demand for real-time detection.

For the current stage of helmet detection in complex environments with low accuracy, missed detection and not easy to manage wearing, this paper proposes a YOLOv5 face helmet detection algorithm based on Swin Transformer improvement from the overall semantics of the image. In this paper, experiments are conducted using a self-built dataset to further enhance the performance of the model and improve the accuracy of face helmet detection through Mosaic data enhancement, label smoothing processing, adaptive weighted features combined with Wconcat module and the application of C3TR and C3STR modules to fuse multi-scale information, enhance the feature extraction capability of the network, and improve the generalization and robustness of the model with a self-built dataset . Experiments show that the improved YOLOv5 face helmet detection algorithm mAP based on Swin Transformer improves 5.7% compared with Faster RCNN, 6.1% compared with YOLOV3, 5.3% compared with YOLOV4, and 1.6% compared with the original algorithm. It performs well in helmet face detection tasks in complex environments, achieving real-time detection and higher accuracy, while reducing missed detections.

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

Smoke detection in foggy surveillance environments is a challenging task and plays a key role in disaster management for industrial systems. The current smoke detection methods are applicable to only normal surveillance videos, providing unsatisfactory results for video streams captured from foggy environments, due to challenges related to clutter and unclear contents. In this paper, an energy-friendly edge intelligence-assisted smoke detection method is proposed using deep convolutional neural networks for foggy surveillance environments. Our method uses a light-weight architecture, considering all necessary requirements regarding accuracy, running time, and deployment feasibility for smoke detection in an industrial setting, compared to other complex and computationally expensive architectures including AlexNet, GoogleNet, and visual geometry group (VGG). Experiments are conducted on available benchmark smoke detection datasets, and the obtained results show better performance of the proposed method over state-of-the-art for early smoke detection in foggy surveillance.

The fire detection system combines the simultaneous measurements of smoke, carbon monoxide, and carbon dioxide. The security of campus against intruders moving in laboratories, class rooms, staffrooms or washrooms. The fire alarm system consists of Fire detectors (with can be smoke detector, heat, or Infra-Red detectors), control unit and alarm system. A fire detection system is developed based on the simultaneous measurements of temperature and smoke. The fire detection system with the alarm algorithm detected fires that were not alarmed by smoke sensors, and alarmed in shorter times than smoke sensors operating alone. Previous fire detection algorithms used data from sensors for temperature, smoke, and combustion products. The smoke sensor alarms when the analog output signal exceeds or equal the threshold value. The node includes analog sensors to measure smoke, carbon monoxide (CO) and temperature. A fire alarm system should reliably and in a timely way notify building occupants about the presence of fire indicators, such as smoke or high temperatures.

With the rapid development of the low-altitude economy and smart city construction, the demand for small target detection has been growing significantly. This paper proposes a deep learning model, MAE-ResTransFPN, for radar target detection tasks in clutter-rich environments. First, real-world measured data is acquired using holographic staring radar, and the Range-Doppler plane is used as the input to the neural network to complete dataset preparation and annotation. Second, the proposed MAE-ResTransFPN model combines the pre-training capabilities of the masked autoencoder (MAE), the feature extraction capabilities of ResNet50, and the multi-scale feature fusion capabilities of Transformer-FPN, aiming to improve the accuracy and robustness of radar target detection in complex environments. Experimental results show that the proposed model achieves 78.2% in precision, 71.4% in recall, and 73.4% in mAP50, demonstrating its effectiveness in radar target detection tasks.

In the current agricultural landscape, a significant portion of tomato plants suffer from leaf diseases, posing a major challenge to manual detection due to the task's extensive scope. Existing detection algorithms struggle to balance speed with accuracy, especially when identifying small-scale leaf diseases across diverse settings. Addressing this need, this study presents FCHF-DETR (Faster-Cascaded-attention-High-feature-fusion-Focaler Detection-Transformer), an innovative, high-precision, and lightweight detection algorithm based on RT-DETR-R18 (Real-Time-Detection-Transformer-ResNet18). The algorithm was developed using a carefully curated dataset of 3147 RGB images, showcasing tomato leaf diseases across a range of scenes and resolutions. FasterNet replaces ResNet18 in the algorithm's backbone network, aimed at reducing the model's size and improving memory efficiency. Additionally, replacing the conventional AIFI (Attention-based Intra-scale Feature Interaction) module with Cascaded Group Attention and the original CCFM (CNN-based Cross-scale Feature-fusion Module) module with HSFPN (High-Level Screening-feature Fusion Pyramid Networks) in the Efficient Hybrid Encoder significantly enhanced detection accuracy without greatly affecting efficiency. To tackle the challenge of identifying challenging samples, the Focaler-CIoU loss function was incorporated, refining the model's performance throughout the dataset. Empirical results show that FCHF-DETR achieved 96.4% Precision, 96.7% Recall, 89.1% mAP (Mean Average Precision) 50-95 and 97.2% mAP50 on the test set, with a reduction of 9.2G in FLOPs (floating point of operations) and 3.6M in parameters. These findings clearly demonstrate that the proposed method improves detection accuracy and reduces computational complexity, addressing the dual challenges of precision and efficiency in tomato leaf disease detection.