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Pomacea canaliculata egg detection method based on multi-scale feature fusion and dynamic convolution

A multi-scale feature and detection method technology, applied in the field of computer vision, can solve the problems of low recognition accuracy of apple snail eggs and poor model robustness, and achieve the effect of improving accuracy, effectively extracting features, and enhancing recognition ability

Pending Publication Date: 2021-11-12
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0006] The invention proposes a method for detecting apple snail eggs based on multi-scale feature fusion and dynamic convolution, which solves the problems of low identification accuracy of apple snail eggs and poor model robustness

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  • Pomacea canaliculata egg detection method based on multi-scale feature fusion and dynamic convolution
  • Pomacea canaliculata egg detection method based on multi-scale feature fusion and dynamic convolution
  • Pomacea canaliculata egg detection method based on multi-scale feature fusion and dynamic convolution

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Embodiment Construction

[0040] The present invention is described in detail below in conjunction with accompanying drawing and specific embodiment:

[0041] Such as figure 1 As shown, a method for detecting apple snail eggs based on multi-scale feature fusion and dynamic convolution includes the following steps:

[0042] Step 1: Obtain a data set, collect aerial images of apple snail eggs, and label the eggs;

[0043] Step 2: Build a neural network, use darknet53 as the backbone network, replace all convolution kernels with dynamic convolution kernels, and extract features more accurately; add a fourth branch for smaller targets, and the features of the other three branches Perform fusion to more accurately locate and identify small targets;

[0044] Step 3: Train the neural network, send the obtained apple snail egg data set into the neural network for training until the network converges;

[0045] Step 4: Image detection, using the trained neural network and weight files to detect apple snail eg...

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Abstract

The invention discloses a pomacea canaliculata egg detection method based on multi-scale feature fusion and dynamic convolution, belongs to the technical field of computer vision, and improves the accuracy of current pomacea canaliculata egg detection. The method comprises the following steps: firstly, collecting aerial images of ampullaria gigas eggs, and marking the eggs in the aerial images; then on the basis of the darknet53 network structure, using four-scale feature fusion and dynamic convolution to construct a new network structure; sending the obtained pomacea canaliculata egg data set into a neural network for training until the network converges, and obtaining a weight file; and then detecting the ampullaria gigas egg target in the test image by using the trained neural network and the weight file, and outputting a detection result. According to the invention, the problems that the current ampullaria gigas egg recognition accuracy is low, and the model has a low ampullaria gigas egg recognition rate in a real natural environment are solved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for detecting apple snail eggs based on multi-scale feature fusion and dynamic convolution. Background technique [0002] The rapid development of computer vision in recent years has made scenarios such as smart agriculture and scientific epidemic prevention a reality. People are increasingly inclined to use computer vision technology to detect potential pest hazards. Most existing object detection methods can only classify eggs, or detect them in specific background conditions rather than natural environments, and they cannot achieve good results in complex real-world scenes. [0003] In terms of pest control, in order to solve the classification problem, Konantinos P. Ferentinos et al. developed a special deep learning model based on the VGG convolutional neural network architecture to identify plant diseases from simple images of healthy or disease...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/23213Y02A90/10
Inventor 刘宁钟徐亚欣寇金桥
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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