Research On Video Smoke Detection Algorithm For Complex Scenes

Fire a great threat to human lifc and property safety.Traditional fire detectors such as temperature,smoke and light sensors have a limited detection range,are vulnerable to external interference and are difficult to adapt to complex environmental requirements,such as forests,farmland,factories or other indoor and outdoor places.Smoke is a prominent visual feature in the early stage of fire,and video smoke detection is of great significance for fire alarm.Therefore,this paper takes smoke videos of complex scenes as the research object,and two smoke detection methods based on traditional feature classification and deep learning are proposed.The main work contents are as follows:(1)In order to solve the problems of low illumination,poor contrast and noise in smoke video in different environments,a global adaptive Retinex image enhancement algorithm is proposed.Firstly,the S component is nonlinear stretched by HSV color model.Then,the V component is enhanced by Retinex algorithm fused with bilateral f iltering.Finally,aiming at the problem of insufficient contrast enhancement for some images,a global adaptive logarithmic enhancement algorithm is introduced for correction.Experimental results show that this method can enhance the contrast of smoke image adaptively and protect the image details while noise is suppressed.For high brightness images,the adaptive function approximates from logarithmic curve to linear curve,which can avoid over enhancement.(2)Aiming at the traditional feature classification methods are prone to background interference and low recognition accuracy,a video smoke detection algorithm based on multifeature fusion and modified random forest is proposed.Firstly,ViBe motion detection and HSV color model are employed to determine the complete candidate smoke area;Then,the wavelet high-frequency energy,smoke growth rate and dynamic texture of smoke are extracted based on the candidate area,and the feature vector is formed after fusion and normalization;Finally,sorting and clustering methods are used to select decision trees with strong classification ability and low correlation,and the modified random forest classifier is constructed.Experimental results show that the modified random forest video smoke detection algorithm has a good recognition effect in complex environment,and the smoke detection rate is 95.2%,which is 3%higher than that of other classifiers.(3)In view of the above traditional detection methods need to manually select features and cannot obtain smoke location information,,an ECA-bneck-YOLOv4 video smoke detection algorithm is proposed,and the detection performance is enhanced by modifying the backbone network and introducing ECA-bneck module.Firstly,standard convolution is replaced by depthseparable convolution,and the number of network parameters is reduced by depthwise convolution and pointwise convolution.At the same time,the number of CSP modules in the backbone network is reduced to improve the detection rate.Then,arming at the dynamic characteristics of smoke in complex scenes,the ECA attention mechanism is employed to suppress the interference from redundant background,and 1 × 1 convolution is added to form an inverted residual structure,which enhances the feature learning ability of the network and improves the accuracy of target detection ultimately.Experimental results show that the proposed method is 3.4%higher than traditional feature classification method,the generalization ability is stronger in complex scenes,and it can also realize fire localization.Compared with similar detection algorithms,the detection frame rate is increased by 9.25 frames per second,which has a higher detection rate.