An Assisted Forklift Pallet Detection with Adaptive Structure Algorithm

• Main Authors: Jia-Liang Syu, 徐嘉良
• Other Authors: Jen-Shiun Chiang
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/35584120378780236404

碩士 === 淡江大學 === 電機工程學系碩士班 === 104 === Nowadays, Industry 4.0 is an important trend in factory automation (FA), which automated-storage-and-retrieval-system (ASRS) is one of the most important issues in the industry. It has been widely used in a variety of industries to handle a variety of storage applications in factories, warehouses, etc. However, the cost to construct an ASRS is too expensive such that most of the small/medium enterprises cannot afford it. A forklift system is another alternative solution to replace the complicated ASRS due to its low cost characteristics. In this work, a new pallet detection based on adaptive structure feature (AS) and direction weighted overlapping ratio (DWO) to aid forklifts in picking up the pallet process model is proposed from a single-lens camera erected at the forklift. Combining AS and DWO for pallet detection, the proposed method can remove most of the non-stationary (dynamic) background and significantly increase the processing efficiency. In our approach, Haar like-based Adaboost scheme with AS of pallets algorithm to detect pallets is presented. It can detect the pallet at luminance less environment. Finally, by calculating the DWO between the detected pallets and tracking record, it can avoid those error responses in object tracking. Therefore, this work improves the pallet detection to solve the problem with an effective design. As results, the hybrid algorithms proposed in this work can increase the average pallet detection rate by 95%.