Application of convolution neural network object detection algorithm in logistics warehouse

Abstract

Because unmanned forklifts need to recognise and locate pallets in warehouses, a detection algorithm based on deep learning framework was proposed. First, the authors collected a large number of pictures including people and pallet in the real warehouse and marked the corresponding label to build a logistics warehouse pallet database. Second, the object detection algorithm based on a single shot multibox detector is improved and trained by the database. In the prediction phase, the network combines the multiscale feature maps with different resolution, which enhances the adaptability of the network to the detection task. Third, the algorithm is an end-to-end detection network, i.e. uses a single network for detection tasks, which can be easily combined with other systems which need detection tasks. The experimental results show that the accuracy of the improved pallet detection algorithm can reach 92.7% and the test rate is 42 frames per second, which can meet the requirements of the efficiency and accuracy of the pallet detection while using the TITAN X GPU.

Keywords

• object recognition
• object detection
• learning (artificial intelligence)
• logistics
• feature extraction
• palletising
• convolutional neural nets
• warehousing
• detection tasks
• multiscale feature maps
• prediction phase
• improved pallet detection algorithm
• detection task
• end-to-end detection network
• single shot multibox detector
• logistics warehouse pallet database
• deep learning framework
• modern logistics warehouse
• object recognition
• convolution neural network object detection algorithm