An image target detection method based on DC-SPP-YOLO

Abstract

The invention discloses an image target detection method based on DC-SPP-YOLO, which comprises the following steps: firstly, preprocessing a training image sample by using a data enhancement method,constructing a training sample set, aSelecting a prior candidate box for target boundary box prediction by using a k-means clustering algorithm; Then, improving the convolutional layer connection modeof the YOLOv2 model from layer-by-layer connection to dense connection, introducing a space pyramid pooling between the convolutional module and a target detection layer, and establishing DC-SPP-YOLOtarget detection model; And finally, constructing a loss function by using an error quadratic sum between the predicted value and the real value, and iteratively updating model weight parameters to converge the loss function to obtain the DC-SPP-YOLO model for target detection. The invention considers the gradient disappearance caused by deepening convolution network and the insufficient use of multi-scale local region features of YOLOv2 model, and constructs a DC-SPP-YOLO target detection model based on improved convolution layer dense connection and spatial pyramid pooling. the target detection accuracy is improved.

Description

technical field [0001] The present invention relates to an image target detection method, which belongs to the technical field of machine vision, and in particular to a target detection method based on dense connection and spatial pyramid pooling YOLO (Dense Connectivity and Spatial Pyramid Pooling Improved You Look Only Once, DC-SPP-YOLO) . Background technique [0002] Object detection is one of the core research contents in the field of machine vision, and it is widely used in driving navigation, workpiece detection, robotic arm grasping, etc. Establish and train a high-quality target detection model, which can extract more abundant and effective target features, and improve the accuracy of locating and classifying targets in images or videos. [0003] Traditional target detection methods such as Deformable Parts Models (DPM) search for target positions through sliding windows, which is inefficient; extracting artificially designed features such as histogram of oriented ...

AI Technical Summary

Problems solved by technology

However, the YOLO and YOLOv2 methods still have limitations in the target detection accuracy; when the model learning ability is improved by deepening the network, the gradient disappearance phenomenon will occur. The YOLOv3 algorithm uses the residual connection method to alleviate the gradient disappearance phenomenon but hinders the information of each layer of the network. At the same time, the multi-scale target detection of YOLOv2 and YOLOv3 algorithms focuses on the fusion of global features of different scale convolutional layers, ignoring the fusion of local area features of different scales in the same convolutional layer; this restricts the improvement of target detection accuracy

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