Gripped target detection algorithm based on edge extraction and cavity convolution

The invention discloses a captured target detection algorithm based on edge extraction and cavity convolution, and the algorithm comprises an Edge feature module, a Darknet-53 feature extraction module, a feature pyramid structure, and a Dilation-ASFF network, and the specific steps are as follows: step 1, image collection: collecting an RGB image of a target, and extracting the edge information of the image by using the Edge feature module to form the feature information of four channels; step 2, feature extraction: inputting the collected feature information into a Darknet-53 feature extraction module, performing feature extraction, improving a loss function into a CIoU loss function, and constructing a feature pyramid structure; and step 3, feature fusion: fusing feature information of different scales in the feature pyramid structure through a Dilatation-ASFF network combined with cavity convolution. According to the method, the detection precision and the detection speed of the network are effectively improved, and the sensing capability of the network to multi-scale information and the convergence capability of the network are improved.