Study on a YOLOv5-DK algorithm for lemon initial pests and diseases detection
In order to solve the problems that the characteristic parts of insect pests in the early stage of lemon were too small and difficult to detect along with the limited data sets,a YOLOv5-DK detection algorithm was proposed.The algorithm was based on YOLOv5,and adopted K-Means++to re-cluster the anchor frame of the initial pest location of lemon,alleviating the problem of small pest characteristics at the initial stage of lemon.Meanwhile,a new lightweight Denseneck-2 module was proposed,which applied the idea of reuse in the DenseNet network,so that the input of each layer of the detection algorithm had the characteristic information of each layer in front of it,which decreased the YOLOv5-DK demand on the initial sample volume of insect pests of lemons.The new YOLOv5-DK detection algorithm demonstrated higher competencies than the original one,including an increase of 3.4％in the average accuracy of detection,a decrease of 2.1％in the missed detection rate,and a reduction of 6.3％in the number of parameters of the algorithm model.These results showed that the algorithm performed better in the application of small samples and small targets.
lemoninitial pests and diseaseslightweightsmall samplesmall target
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