The economy developed rapidly and the change in consumption segment poses higher requirements for the efficient circulation of industrial product transportation. Improving the speed of goods circulation and ensuring the safety and quality of finished products has become an important issue. However, relevant research on these is still lacking. To address the issues of missing barcodes, box damage, and brand error in existing industrial products within the park, this paper proposes an industrial packaging surface barcode and damage detection method based on the GooLeNet network. First, Delta Machine Vision (DMV) products capture and quickly read barcodes from six directions. Second, a corresponding training set is created through sample and data collection. The training set categorizes damage into three types; damaged holes, cracks, and indentations. Moreover, image enhancement processing, along with data expansion, is applied to the dataset. Finally, the improved GooLeNet network model is designed by combining GooLeNet architecture with regularization, aiming to facilitate feature extraction and training of images under the interference of packaging surface patterns. This design leads to a higher damage identification accuracy of 96.63%, which is 14.66%, 3.72%, 14.05%, and 12.78% higher than that of AlexNet, GoogleNet, VGG, and RestNet, respectively, in a convolutional neural network.