# Development of Smart and Lean Pick-and-Place System Using EfficientDet-Lite for Custom Dataset
## 1 Introduction  
One of the current trends in advanced manufacturing is to employ Artificial Intelligence (AI) methods to improve the pick-and-place process. The integration of AI and machine vision with pick-and-place operations can significantly improve the manufacturing process. It should be made affordable for Small and Medium Enterprises (SMEs) so that they can leverage the benefits come with this technology without being concerned with allocating a significant financial budget. The fast and smooth integration of machine vision technology with the current pick-and-place operations of SMEs is another crucial aspect that should be taken into consideration. In this context, any machine vision solution should be developed in a way that the commissioning and installing can be carried out simply and quickly by the field operators of SMEs without special skills. Therefore, one of the current trends in advanced manufacturing is to employ object detection using Deep Learning methods to improve the pick-and-place process. Furthermore, we study the usage of this smart and lean system in low-light environments, such as waferlab manufacturing during the night shift. Die-sorting machines, for example, can be used in low-light environments because they are totally automated and require no user intervention. As such, sufficient ambient lighting is not needed [1], and hence, our aim is to develop a smart and lean pick-and-place solution, which requires minimal computational resources, for custom workpieces. AI model efficiency has become increasingly important in computer vision, and object detection using machine learning is becoming crucial for pick-and-place operation and warehousing logistics [2; 3]. Other than allowing the smart detection of objects withoutcamera recalibration, AI enhances the application's robustness to light changes, shadow, background noise and low-illumination [4]. Continuing from our previous work [5], we aim to improve the pick-and-place operation using an Artificial Intelligence model deployed on an embedded controller. Our target is to develop a smart and lean pick-and-place system that meets the requirements of low power consumption, small memory usage, and fast run time. Model efficiency has become increasingly important in computer vision to be used on mobile devices and embedded controllers such as Raspberry Pi due to the model's compact size and quick inference. Convolutional Neural Network (CNN) models are crucial for feature extraction and classification, but conventional high-performance algorithms are expensive due to their high-power consumption and memory usage. TensorFlow has developed several deep learning object identification models that were designed for embedded and mobile devices, hence the name TensorFlow Lite (TFLite). The model's compact size and quick inference for TFLite versions make them appropriate for a variety of applications, including wearable technology and the Internet of Things. A pre-trained TensorFlow model is used to act as a learnt generic model because it has previously been trained on a sizable general dataset. This helps to reduce the quantity of data required to train a model from scratch. Our project uses the state-of-the-art model EfficientDet-Lite [6], which was developed by Google in 2020. Compared to other state-of-the-art models, EfficientDet-Lite detectors are more precise and need fewer computational resources (working memory, power consumption, and floating-point operations per second or FLOPS) than their predecessors. TFLite is a model optimization toolkit provided by Google, whose purpose is also to reduce the complexity of deep learning models and speed up the inference time. TFLite's key feature is size reduction, which results in a smaller model and less need for storage space and RAM. TFLite decreases latency by quantization, which helps to simplify calculations during inference with only a little amount of accuracy loss. TFLite optimizes the model via quantization, clustering, and pruning.