# Vision System Experimentation in Furniture Industrial Environment
## 2 Literature Review  
The automation of code recognition has become both a necessity and a challenge in industrial environments, considering the adverse conditions related to the orientation, illumination and quality of images that can be found. Computer vision algorithms can be implemented to read and decode these codes accurately and overcome these difficulties [1]. All these aspects are mainly focused on readability, which affects the interpretation of codes regardless of the industrial sector. Intensive studies and trials have been performed in order to reduce errors and improve reading time [2]. The placement of the scanning system is also critical, depending on the environment. For activities such as price calculation, handheld devices are considered an optimal approach, but in situations where the volume of information is high and readings should be rapidly taken, this is not a proper solution. The software that comes with the scanning system usually simulates the behavior of the handheld device, tracing lines through the laser beam in order to calculate the dimensions of the code to be scanned. In order to improve this mechanism and fix the noise problems that lead to poor quality for code recognition, a Hough transform approach can be adopted [3]. The software that supports code reading usually uses specific algorithms to improve the reading ability of the system. The practical experience exposed in this paper assumes nearly constant values for the position, lighting, and distance of the scanning system during the management of the pieces within acceptable ranges for the interpretation of codes, so the manufacturer generally maintains regular conditions during the workflow. Furthermore, the hardware deployed for the implementation of the vision system is flexible enough to save any minor program changes in these parameters. Vision systems are widely used in smart factories in the scope of Industry 4.0, where information from industrial equipment is collected. This allows a higher level of integration in the production environment, which, in turn, becomes more flexible. Relevant examples include production environments, where the identification technology involves local decoding software, enabling communication between the production line and the server, improving production efficiency and reducing errors [4]. In addition to QR codes and barcodes, a significant improvement for the identification of pieces is the use of smart labels containing embedded modules to enable communication via wireless and integration with sensors [5]. Thus, the practical experimentation covered in this paper considers the factory as a single production unit. The retrieval of piece information from an ERP system is enough to obtain the status of the piece and its track, providing full traceability throughout the factory. Furthermore, for several reasons, barcode technology is adopted instead of Radio Frequency IDentification (RFID). RFID is a wireless communication technology that allows capturing data that may be linked to identification attributes (i.e., serial number, position, color, date) of items carrying labels. Its use is increasing, and its performance is being refined [6]. However, in the production environment, which is the focus of this experiment, the use of barcodes is preferred as the RFID technology may present problems with the metal rollers of the edge-banding machine. The shift of the resonance frequency and the use of metallic objects in RFID applications often degrade the efficiency and readable range of the tag antennas. To overcome these problems, researchers have proposed different approaches [7]. However, many of the standard solutions currently available do not guarantee compatibility with this type of environment. Furthermore, the resistance and durability of the barcode labels are higher than the RFID, and its deployment, considering that this is the current implementation in the selected factory, is less costly.