# EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems
## 1 Introduction  
In supply chain management, the movement of materials and goods through several network points demands manual checks at each location. Inspections are conducted to ensure that the items being moved are correctly identified and the instructions are checked for completeness. In conventional manufacturing, stacked package recognition/movement is still a task performed by people. Owing to the time-consuming and complex factory environment, manual operations are no longer sufficient to meet production needs [1, 2]. Automation is increasingly used across various scenarios, driven by the rapid expansion of the modern manufacturing industry. According to current trends in manufacturing and logistics, the fastest, most efficient, and least expensive system prevails in terms of logistics productivity. Since automated guided vehicles (AGVs) can operate autonomously and move things back and forth during manufacturing, they can be employed in factories to improve efficiency and quality. AGV systems function autonomously, requiring no human involvement, such as drivers. Software and sensor-based guidance systems operate together to manage their logistics. The usefulness of AGVs is that they can stimulate production by efficiently using inventory space and accelerating the logistics throughautomation. Additionally, because work is conducted along a prearranged route, the chance of an accident occurring can be significantly decreased [2; 3]. Containers are used to move material between locations during manufacturing. In logistics, AGVs will move containers to a specific location based on the instructions. Since many types of containers need to be recognized by an AGV in a factory, the necessity for precision, stability, and adaptability in AGV systems is important. Thus, vision-based technology is an ideal solution as deep learning and computer vision technologies continue to improve [4]. To enable vision-based technology, we provide an automated system for container recognition. This approach facilitates the sorting of containers, allowing them to be classified and counted more accurately and effectively throughout the manufacturing process. RFID technology, along with integrated IT platforms, is used in logistics organizations for warehousing needs. RFID utilizes electromagnetic fields to automatically recognize and monitor tags attached to items. RFID tags, especially passive ones that do not require batteries, are highly recognized, robust, and energy-efficient, guaranteeing long-term reliability and cost-effectiveness. RFID technology has advantages in inventory management, while vision-based techniques provide more promising benefits. RFID technology offers detailed data linked to each tag, including color, measurements, and price, which is advantageous for improving inventory control. Nevertheless, this technology has limitations regarding its flexibility and adaptability. RFID systems depend on certain tags requiring appropriate infrastructure and have difficulty adjusting to environmental variations or different object categories. RFID technology may have lower accuracy in real-time object detection, which could impact the speed of responding to inventory changes. Vision-based techniques provide flexibility, adaptability, and high precision in object detection. Images, especially those in color, provide extensive information that surpasses that of other sensors. In addition to providing distance information, they also recognize an object's color, texture, and depth. Vision systems may adjust to different environments and object categories without relying on specific tags, providing real-time and detailed information about inventory items. Additionally, vision-based technologies enable immediate responses to changes in the inventory status, making them superior options for modern logistical operations [1; 5; 6; 7]. Automated package recognition systems have been developed by many companies. For example, a system developed by Logivations uses an automated method to identify products and their quantities.