The different imaging conditions of high spatial resolution remote sensing images(HSRRSIs)tend to cause large differences in the background information of bridges from the images,including problems of difficult detect...The different imaging conditions of high spatial resolution remote sensing images(HSRRSIs)tend to cause large differences in the background information of bridges from the images,including problems of difficult detection of multiscale bridges,leakage of small bridges and insufficient detection accuracy for their detection.To address these problems,a YOLOv5 network with a decoupled head for the automatic detection of bridges in HSRRIs is proposed in this paper.First,the problem of inconsistent scale of information fusion of each feature in the feature pyramid network is solved using a weighted bi-directional feature pyramid network(BiFPN).Then,the convolutional block attention module(CBAM)is fused into the three effective feature layers after feature pyramid network processing.The bridge feature information is effectively extracted from the channel and spatial dimensions.Next,the decoupled head is fused in the YOLO Head to separate the classifier and regressor to speed up the network convergence and improve the network detection accuracy simultaneously.Finally,the practical effect is evaluated by calculating the average precision(AP).According to the experimental results,the AP of the proposed method is 98.1%,which is improved by 4.1%∼23.5%compared with other models.展开更多
Underwater object detection technology is essential for maintaining marine ecological health and supporting economic development.However,the underwater environment poses significant challenges,including low contrast,s...Underwater object detection technology is essential for maintaining marine ecological health and supporting economic development.However,the underwater environment poses significant challenges,including low contrast,small object sizes,and complex backgrounds.Existing generic object detectors often fail to identify these organisms effectively.This paper proposes a Joint Multi-scale channel attention and Multi-perception head Network(JMM-Net),a detection algorithm for underwater organisms.JMM-Net comprises three main components:Multi-Scale Channel Attention(MSCA)-based backbone network,Multi-Perception Parallel detection head(MPPhead),and lightweight GSconv-Path Aggregation Network(GS-PAN).MSCA is embedded into the backbone to enhance feature extraction for blurred and small-sized objects in low-quality environments by integrating local and global channel attention through multi-scale parallel sub-networks and cross-channel learning.MPPhead enhances the model's classification and localization capabilities by leveraging scale,spatial,and task perception,thereby enhancing the detection of marine organisms in complex backgrounds.The adoption of GS-PAN over the traditional Path Aggregation Network(PAN)structure significantly reduces the model's parameters and computational load,making it more suitable for deployment on edge devices.Extensive experiments on three public underwater datasets demonstrate that our method achieves excellent performance on underwater object detection at a lightweight cost.展开更多
A forest fire is a natural disaster characterized by rapid spread,difficulty in extinguishing,and widespread destruction,which requires an efficient response.Existing detection methods fail to balance global and local...A forest fire is a natural disaster characterized by rapid spread,difficulty in extinguishing,and widespread destruction,which requires an efficient response.Existing detection methods fail to balance global and local fire features,resulting in the false detection of small or hidden fires.In this paper,we propose a novel detection technique based on an improved YOLO v5 model to enhance the visual representation of forest fires and retain more information about global interactions.We add a plug-and-play global attention mechanism to improve the efficiency of neck and backbone feature extraction of the YOLO v5 model.Then,a re-parameterized convolutional module is designed,and a decoupled detection head is used to accelerate the convergence speed.Finally,a weighted bi-directional feature pyramid network(BiFPN)is introduced to merge feature information for local information processing.In the evaluation,we use the complete intersection over union(CIoU)loss function to optimize the multi-task loss for different kinds of forest fires.Experiments show that the precision,recall,and mean average precision are increased by 4.2%,3.8%,and 4.6%,respectively,compared with the classic YOLO v5 model.In particular,the mAP@0.5:0.95 is 2.2% higher than the other detection methods,while meeting the requirements of real-time detection.展开更多
基金funded by National Natural Science Foundation of China[grant nulmber 41961039]Yunnan Fun-damental Research Projects[grant numbers 202201 AT070164,202101AT070102].
文摘The different imaging conditions of high spatial resolution remote sensing images(HSRRSIs)tend to cause large differences in the background information of bridges from the images,including problems of difficult detection of multiscale bridges,leakage of small bridges and insufficient detection accuracy for their detection.To address these problems,a YOLOv5 network with a decoupled head for the automatic detection of bridges in HSRRIs is proposed in this paper.First,the problem of inconsistent scale of information fusion of each feature in the feature pyramid network is solved using a weighted bi-directional feature pyramid network(BiFPN).Then,the convolutional block attention module(CBAM)is fused into the three effective feature layers after feature pyramid network processing.The bridge feature information is effectively extracted from the channel and spatial dimensions.Next,the decoupled head is fused in the YOLO Head to separate the classifier and regressor to speed up the network convergence and improve the network detection accuracy simultaneously.Finally,the practical effect is evaluated by calculating the average precision(AP).According to the experimental results,the AP of the proposed method is 98.1%,which is improved by 4.1%∼23.5%compared with other models.
文摘Underwater object detection technology is essential for maintaining marine ecological health and supporting economic development.However,the underwater environment poses significant challenges,including low contrast,small object sizes,and complex backgrounds.Existing generic object detectors often fail to identify these organisms effectively.This paper proposes a Joint Multi-scale channel attention and Multi-perception head Network(JMM-Net),a detection algorithm for underwater organisms.JMM-Net comprises three main components:Multi-Scale Channel Attention(MSCA)-based backbone network,Multi-Perception Parallel detection head(MPPhead),and lightweight GSconv-Path Aggregation Network(GS-PAN).MSCA is embedded into the backbone to enhance feature extraction for blurred and small-sized objects in low-quality environments by integrating local and global channel attention through multi-scale parallel sub-networks and cross-channel learning.MPPhead enhances the model's classification and localization capabilities by leveraging scale,spatial,and task perception,thereby enhancing the detection of marine organisms in complex backgrounds.The adoption of GS-PAN over the traditional Path Aggregation Network(PAN)structure significantly reduces the model's parameters and computational load,making it more suitable for deployment on edge devices.Extensive experiments on three public underwater datasets demonstrate that our method achieves excellent performance on underwater object detection at a lightweight cost.
基金supported by the Graduate Research and Innovation Projects of Jiangsu Province(No.SJCX23_0320).
文摘A forest fire is a natural disaster characterized by rapid spread,difficulty in extinguishing,and widespread destruction,which requires an efficient response.Existing detection methods fail to balance global and local fire features,resulting in the false detection of small or hidden fires.In this paper,we propose a novel detection technique based on an improved YOLO v5 model to enhance the visual representation of forest fires and retain more information about global interactions.We add a plug-and-play global attention mechanism to improve the efficiency of neck and backbone feature extraction of the YOLO v5 model.Then,a re-parameterized convolutional module is designed,and a decoupled detection head is used to accelerate the convergence speed.Finally,a weighted bi-directional feature pyramid network(BiFPN)is introduced to merge feature information for local information processing.In the evaluation,we use the complete intersection over union(CIoU)loss function to optimize the multi-task loss for different kinds of forest fires.Experiments show that the precision,recall,and mean average precision are increased by 4.2%,3.8%,and 4.6%,respectively,compared with the classic YOLO v5 model.In particular,the mAP@0.5:0.95 is 2.2% higher than the other detection methods,while meeting the requirements of real-time detection.
