# Development of Smart and Lean Pick-and-Place System Using EfficientDet-Lite for Custom Dataset
## 1 Introduction
The model's accuracy is reduced, as it is quantized from FP64 to lower resolutions (FP32, FP16, INT8). In order to reduce a model's complexity by reducing the number of its unique weights, clustering involves dividing the weights of the trained model's layers into clusters and sharing the centroid of each cluster's weight. Pruning is the process of removing less important model parameters that barely affect the outcomes of predictions. This aids in simplifying the model at the expense of some accuracy loss and offers far higher performance. EfficientDet uses the same backbone as EfficientNet and adds a bi-directional feature pyramid network (BiFPN) to help in multi-scale feature fusion. It utilizes several optimization and backbone tweaks and a compound scaling method that uniformly scales the resolution, depth and width for all backbones, feature networks and box/class prediction networks at the same time. Considerable research has already been completed for object detection using AI, laying the foundation for this work. The contribution of every work published previously in all the relevant domains has played a significant role in developing this work. Our pick-and-place approach is similar to a robot collecting a rock from a hopper using a vision-based image processing algorithm [7] and a robot performing pick and place operations on deformable items [8]. Similar to us, some researchers developed novel methods to improve the mean Average Precision. Using Faster-RCNN, [PERSON] [1] developed a vehicle detection approach for insufficient and night-time illumination conditions and improved the mAP values by 0.2; however, it was not meant for a resource-constrained embedded system. [PERSON] [9] achieved an average detection accuracy of 57.51% on EfficientDet-D2 for the road damage detection, which was lower than our 74.1% AP for the Alphal dataset. [PERSON] [10] developed \"DeepSeaNet\" to detect underwater objects with EfficientDet with a high accuracy of 98.63%, but the method was not suitable for lightweight devices due to the complexity of the model. Similar to our lightweight methodology, [PERSON] [11] used EfficientDet-4 on Raspberry Pi to monitor the codling moth population and achieve a high accuracy of 99% with a small dataset of 430 images. [PERSON] [12] adopted EfficientDet to detect textureless objects in an industrial environment, but this method was not suitable for Raspberry Pi due to the heavy computation. [PERSON] [13] used a web camera and ABB robot to detect color and perform a color-sorting algorithm; this work has 100% detection but no machine learning implementation. [PERSON] [14] used another lightweight model, SSD MobileNet V2, on Raspberry Pi to detect barriers for blind people to navigate safely. EfficientDet is used in non-industrial scenarios such as dental application. [PERSON] [15] assessed the diagnostic quality of bitewing radiographs at contact areas between teeth, which can help the oral radiologists provide better radiographic qualities. Other than EfficientDet, advanced CNN is used in other applications. For example, it is used in implementing traffic signs recognition in a mobile-based application [16], a speech recognition system using TensorFlow [17], a smart surveillance system for night low-illumination objects [18] and a visual feedback algorithm on AlexNet [19]. The effectiveness of illumination to improve the detection scores has not been extensively studied for pick-and-place solutions, especially robots used in warehouses and logistics areas with low illumination, and thus, our research aims to fill this gap. We implement a smart and lightweight object detector requiring minimal computational resources which controls a pick-and-place system. The illumination effect on the EfficientDet-Lite model deployed in Raspberry Pi are investigated and compared. Our contributions include the following:  
* The addition of 8% optimized bright Alpha3 images resulted in a 7.5% increase in Average Precision and a 6.3% increase in F1-score.