# Pallet Recognition with Multi-Task Learning for Automated Guided Vehicles
## 1 Introduction  
Automated guided vehicles (AGVs) are one of the most important elements of manufacturing systems. They have developed steadily since the 1950s as they provide a flexible and reliable alternative to conventional material transportation. For repetitive transportation, AGVs were previously guided by wires embedded in the floors. In recent years, laser navigation has allowed AGVs to travel more diverse and accurate routes and has been extended in logistics systems to guide automated forklifts. The advantage of AGVs is that they can increase the speed of logistical circulation through automation and increase productivity through efficient use of inventory space. Moreover, the risk of workplace accidents can be greatly reduced because work is performed along a planned route. Consequently, AGVs can be used in warehouses in which repetitive work is frequent, others that require unmanned operation 24 h a day, and in those whose narrow spaces demand efficient use of space. In particular, AGVs play essential roles in smart factories where all facilities and devices are interconnected by wireless communication. Pallets are used to transport objects stably, and moving them has become the main role of AGVs. An AGV moves to a specified pallelet, lifts it using forks, and transports it to a specific location. All processes are performed based on an assumption that the pallets are centered and squared so an AGV can move straight forward to lift the pallelet. However, repeated lifting of pallets with different weights can change the position of the pallelets. If this goes unrecognized, an accident may occur during the lifting or transporting; even if the AGV can recognize the problem, human assistance is still required to execute the liftaccurately. If an AGV is to solve such problem alone, it requires the capability not only to recognize a wrongly positioned pallet, but also the capability adjustments to ensure correct lifting. Several studies have been conducted with visionary sensors with the goal of building an automated forklift system incorporating pallet recognition and forklift control. [PERSON] et al. presented a vision-based system for intelligent forklifts including pallet recognition [1]. An intelligent forklift called ROBOLIFT moves using vision navigation and recognizes pallets through a visionary sensor. It became the basis for developing pallet recognition technology related to computer vision. [PERSON] and [PERSON] presented a vision-guided control method called mobile camera-space manipulation (MCSM) [2]. It enabled the movement of an autonomous forklift based on its actual current location and also recognized the position of pallet by using feedback from the vision sensor in the autonomous forklift. Both studies assumed the pallet was placed on the floor and focused on the control of the forklift. Several studies have focused on pallet recognition. For more accurate pallet recognition, they used images from various sources for feature extraction. [PERSON] et al. proposed a pallet image detection method using a hue saturation value (HSV) space which is different from the red green blue (RGB) space [3]. They used edge detection to classify pallets in photos and used a coordinate system to obtain their location information. They calculated the position of pallets relative to the forklift based on pallet midpoints determined by a camera space model that uses coordinate systems to construct the relationship between the image and real-world spaces. [PERSON] and [PERSON] suggested a system of pallet detection based on the recognition of candidates of pallet edge [4]. Furthermore, to be invariant and robust to certain image brightnesses, they used a gray image scale. [PERSON] et al. also proposed pallet detection and localization using a red green blue depth (RGB-D) camera [5]. Multiple pallets can be recognized at once by using the depth information from an RGB-D camera. Obtaining and processing images in pallet detection was intensively studied before research began on a deep learning approach.