## 1 Introduction  
One of the current trends in advanced manufacturing is to employ Artificial Intelligence (AI) methods, specifically in the pick-and-place process with machine vision. Advanced manufacturing with AI should be made affordable for Small-Medium Enterprises (SMEs), so that they can leverage the benefits that come with this technology without being concerned about 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. Moreover, the training and deployment process of the AI model needs to be fast to reduce the turnkey project time for the SMEs, as time savings are critical for SME [1]. In this context, any machine vision solution should be developed in a way that the commissioning and installation can be carried out simply and quickly by the field operators of SMEs without special skills or a high-powered computer. Therefore, one of the current trends in advanced manufacturing is to employ object detection using deep learning methods on embedded systems to improve the pick-and-place process. Object detection using deep learning has developed greatly within the past few years. The use of Convolutional Neutral Networks (CNNs) and object detection is becoming increasingly important in pick-and-place operations. With object detection, the pick-and-place application is more robust against any varying parameter such as lighting, shadow, and background noise. Other than this, there is a need for lightweight models in instances where hardware limitations exist, particularly in a low-cost embedded system such as Raspberry Pi. The Single Shot Detector (SSD) MobileNet V2 Feature Pyramid Network [2] is one such algorithm. The reason is that it has low-cost computation with higher detection speed compared with other algorithms [3; 4]. SSD is a model that balances the detection accuracy of Faster R-CNN [5] and real-time performance by using multiple feature maps at different scales. This multiscale approach allows SSD to achieve a higher detection accuracy than You Only Look Once (YOLO) [6] while maintaining real-time performance. MobileNetV2 [7] is a lightweight convolutional neural network architecture for mobile devices, making them suitable for real-time applications and edge device deployment on Raspberry Pi. In the architecture of SSD MobileNetV2 FPN-Lite, MobileNetV2 is used as a base network, SSD as a detection network, and FPN-Lite as a feature extractor. Figure 1 shows that MobileNet V2 has three layers with two kinds of a block. The first block is a residue block with stride 1 while the second block is a stride 2 layer that is used to reduce the size. The first layer of MobileNet V2 is a \(1\times 1\) convolution layer with a Rectified Linear Unit with an upper cut-off at 6 or ReLU6, the second layer is a depth-wise convolution layer, and the third layer is a \(1\times 1\) convolution layer without non-linearity. Many studies have already been conducted on object detection utilizing neural network hyperparameter tuning to increase model accuracy. Increasingly, SSD320 Mobilenet V2 FPNLite is being implemented in various applications such as road damage detection [8], traffic density [9], vulnerable road users [10], and real-time road-based object detection [11]. For robotic automation, SSD320 Mobilenet V2 FPNLite is used in various industries such as agriculture [12, 13] and hospitality [14, 15]. Many machine learning models previously developed had very large datasets that are not suitable for devices in the edge running real-time applications. So far, not much research has been conducted on SSD MobileNet V2 FPN320-Lite on pick-and-place application in Raspberry Pi, especially on the hyperparameter and image enhancement optimisation, and thus our research aims to fill this gap.