With the continuous reform of intelligent manufacturing, industrial production has gradually developed from automation to intelligence. The fusion of vision technology and industrial machines has become a hot research direction in current intelligent transformation. However, machines are not as flexible as humans when grabbing, and still have great limitations. Affected by various characteristics of target objects, such as shape, material, weight and other factors, as well as complex and changeable environmental factors, the research of machine grabbing still faces severe challenges. For the actual complex working conditions, the poor target detection effect leads to the inability to complete accurate grabbing, which affects the production efficiency. This paper proposes a grabbing system with convolutional neural network, which can achieve target detection, classification, positioning and grabbing tasks. First, by comparing the current mainstream target recognition and detection algorithms, select SSD that have both real-time performance and accuracy. Then make specific network structure improvements according to the detection requirements, and insert the Inception structure. At the same time optimize its loss function and nonmaximum suppression. The improved recognition rate is higher, and the target detection frame is closer to the real part, which greatly reduces the recognition error. Second, this research proposes an algorithm model for regional posture detection and grabbing positioning, which uses the output of the previous stage as input to perform posture detection and grabbing positioning of the grabbed target. In the network, the posture angle of the grabbing target is output in a classified manner, and the position coordinates of the grabbing point are output using a regression method. Experiments have proved that our method can perform efficient target recognition and grabbing positioning.
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