基于YOLOv8n改进的蚕虫检测与计数方法

Detecting and counting silkworms using improved YOLOv8n

为解决桑蚕养殖过程中对蚕虫计数的难题,特别是针对蚕虫目标小、分布密集且易被遮挡的问题。改研究提出了一种基于YOLOv8模型改进的蚕虫检测与计数新方法(SDM-YOLO)。该方法的核心创新包括:1)引入RCS-OSA模块作为残差模块,替代原YOLOv8中的C2f模块,以增强网络的多尺度特征提取能力并融合不同感受野的信息,提升对密集分布蚕虫的识别能力;2)改造检测头为动态预测头(dynamic prediction head),结合尺寸、空间和通道三个维度的特征信息,提高蚕虫识别的精确度,减少误检;3)优化损失函数,采用EIOU LOSS作为边界框回归的损失函数,以改善密集场景下蚕虫目标的漏检问题。经过试验验证,SDM-YOLO方法在多个评估指标上均表现出色。具体而言,该方法在精确度上达到了88.2%,召回率为87.2%,平均准确度mAP@0.5为93.2%,而mAP@0.5:0.95也达到了74.7%。这些结果充分证明了与一阶段检测模型YOLO系列相比,SDM-YOLO在蚕虫检测与计数方面具有比较明显的优势。

Sericulture has a pivotal position in the agricultural production of China.The number of silkworms is one of the key indicators in the process of silkworm breeding,which is crucial to the feeding and healthy growth of silkworms.Accurate detection of the number of silkworms can ensure the quality of sericulture products.However,manual counting has not fully met the needs of large-scale production in recent years.There is a high demand to combine AI technology for silkworm counting.In this study,silkworm detection and counting were studied from the perspective of deep learning.Several challenges were solved in the detection of silkworms.Since the silkworms belonged to the small targets,it was difficult to detect the fewer features;When the number of silkworms was high,serious masking occurred to cause interference in the detection of densely distributed silkworms;It was difficult to ensure that the clear images of silkworms were captured from the actual production.The blur images were easy to deteriorate the accuracy of detection.Two tasks were covered as follows.1)The image dataset of silkworms was constructed;2)MAM-YOLO,a silkworm detection and counting model was proposed using multi-dimensional perception.The pictures of silkworms were first taken from the Guangxi Sericulture Technology Promotion Station.These pictures of silkworms were preliminarily processed.The polygon annotation was then selected,according to the body and distribution of silkworms.The pictures of silkworms were annotated with the open-source tool(Labelme).Finally,the dataset was expanded using image transformation and noise addition.The silkworm picture dataset was obtained for the subsequent training,verification,and testing.An improved YOLOv8(MAM-YOLO)was proposed to detect the silkworm,in order to realize the accurate detection and counting.A silkworm detection and counting model was proposed using space-to-depth transformation and image deblurring.The fine-grained detection performance of blurred images was improved in the MAM-YOLO model.The space-to-depth transformed convolution was utilized instead of stride convolution for the pooling layer in the feature extraction.The fine-grained features were extracted from the images,in order to further improve the detection of the low-resolution and small targets.In addition,the impact of blurred images on the detection performance was reduced by introducing a deblurring module into the model.RCS-OSA module was added to the YOLOv8 model.The network feature extraction was improved to integrate the information of different sizes in the receptive fields.The detection head of the original network was improved into the multi-dimensional sensing head.The three-dimensional features of size,space,and channel were aggregated to enhance the detection of silkworm targets.The loss function was replaced with the EIOU loss function.The regression accuracy of the detection frame was improved to reduce the object occlusion.Experimental results show that the MAM-YOLO more accurately detected the silkworm targets.The detection speed of mAP@0.5:0.95 reached 74.8%and 22.2 frames per second,indicating the better functions of silkworm detection and counting with high precision and high rate.The detection requirements were fully met in the actual work of the Guangxi sericulture station.The results show that the silkworm detection and counting system of the technical indicators met the application requirements.The finding can provide the detection services for the actual production work of the silkworm breeding station.In the end,a generalization experiment was conducted on the Global Wheat Challenge 2021 dataset.It was found that the MAM-YOLO was also achieved in the better detection performance of wheat ears,particularly on the small targets similar to dense occlusion.Sericulture has a pivotal position in China's agricultural production.The number of silkworms is a key indicator in the process of silkworm breeding,which is crucial to the feeding and healthy growth of silkworms,and accurate detection of the number of silkworms can ensure the quality of sericulture products.The traditional way of counting silkworms manually is inefficient,and it needs to be combined with technology to intelligently complete this kind of heavy work.In this thesis,from the perspective of deep learning,a silkworm detection and counting method is studied,and several problems in silkworm detection are solved:silkworms belong to small targets,which have fewer features so that they are not easy to detect;silkworms are densely distributed,and when the number of silkworms is high,serious masking phenomenon occurs which causes interference in the detection;it is difficult to ensure that the images of silkworms obtained in the actual production are clear,and blur images will make the detection affected.The main research content of this thesis is as follows:This chapter completed two main tasks:1)Completed the production of silkworm image data set;2)MAM-YOLO,a silkworm detection and counting model based on multi-dimensional perception,was proposed.In this paper,pictures of silkworms were taken by workers from Guangxi Sericulture Technology Promotion Station.First,these pictures of silkworms were preliminically processed.Then,according to the characteristics of the body and distribution of silkworms,the polygon annotation method was selected,and the pictures of silkworms were annotated with the open-source tool Labelme.Finally,the data set is expanded by means of image transformation and noise addition,and finally the silkworm picture data set is obtained for subsequent training,verification and testing.This chapter proposes an improved silkworm detection method based on YOLOv8,MAM-YOLO,which realizes accurate detection and counting of silkworm.Secondly,aiming at the problems of loss of fine-grained information and poor detection performance of blurred images in the MAM-YOLO model,a silkworm detection and counting model based on space-to-depth transformation and image deblurring is proposed.The model utilizes space-to-depth transformed convolution instead of stride convolution and pooling layer in the feature extraction process,so that fine-grained features in the image can be extracted,which further improves the detection capability for low-resolution and small targets.In addition,the impact of blurred images on the detection performance is reduced by introducing a deblurring module into the model.By adding RCS-OSA module to YOLOv8 model,the capability of network feature extraction is improved,and the information of different size receptive fields is integrated.The detection head of the original network is improved into multi-dimensional sensing head,and the three-dimensional characteristics of size,space and channel are aggregated to enhance the detection ability of silkworm targets.By replacing the loss function with EIOU loss function,the regression accuracy of detection frame is improved and the problem caused by object occlusion is reduced.Experiments show that MAM-YOLO can more accurately detect silkworm targets,and the detection speed of mAP@0.5:0.95 reaches 74.8%and 22.2 frames per second,which realizes the functions of silkworm detection and counting with high precision and high rate,and can meet the detection requirements in the actual work of Guangxi sericulture station.results show that the silkworm detection and counting system of the technical indicators have met the application requirements,and can provide detection services for the actual production work of silkworm breeding station.In the end,a generalization experiment was conducted on the Global Wheat Challenge 2021 data set,and it was found that MAM-YOLO also showed a high improvement in the detection performance of wheat ears,proving that the proposed method can generalize to other small targets similar to dense occlusion.