# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 1 Introduction
### Related Work in Vision Detection  
In disassembly processes, it is crucial to identify fasteners, which are connectors used to join and hold various parts together. For direct recycling methods, it is critical to identify the locations of these fasteners so the parts they are holding together can be released and reused. In the robotic system designed by [PERSON] and [PERSON] [22], two webcams were used; one mounted above the workspace to locate screws using a Hough circle transform and the other mounted on the robot to verify screws by varying computer vision parameters. [PERSON] et al. [33] used an adaptive thresholding technique to expose contour information. An approach by [PERSON] et al. [34] described a technique that combined contour identification and principal component analysis (PCA) with eigentemplates to identify car wheels and their bolts. [PERSON] et al. [35] proposed a method that also used a PCA approach with a Harris detector to detect the edges and corners of the screw. Object detection, the act of finding specific objects in images, is playing an ever-increasing role in various industries and applications [36]. Ever since [PERSON] et al. [37] introduced AlexNet in 2012, convolutional neural networks (CNNs) have become the predominant method to perform object detection, and they have spawned advanced architectures such as Faster Regions with CNN (R-CNN) [38], You Only Look Once (YOLO) [39], and the Single Shot MultiBox Detector (SSD) [40], amongst others [41]. Recently, deep learning methods in object recognition have started to be employed to locate and identify fasteners. [PERSON] and [PERSON] [42; 43] presented an approach using Hough transforms and two deep CNNs for the detection of screws of variable shapes and sizes on computer hard drives. They designed and deployed an integrated model using InceptionV3 and Xception that generated an average precision of 80.23%. [PERSON] et al. [44] explored screw detection on general electronics using the tiny-YOLOv2 model, specifically how to optimize the hyperparameters of mini-batch size and resolution for best results. They determined that, for the GPU they were using, a mini-batch size of 16 and input resolution of 1664 \(\times\) 1664 px produced a mean average precision (mAP) greater than 90% with all their test sets. [PERSON] et al. [45] investigated using pre-processing adjustments (brightness and contrast) on screws for use with a model pre-trained with the Common Objects in Context (COCO) dataset using Faster R-CNN with the Inception v2 framework. The results were then compared against common screw locations on products to find any undetected screws. [PERSON] et al. [46] used a Faster R-CNN network to find screws from complex mobile phone boards. They also used a rotation edge similarity algorithm to improve the location accuracy of the results and reported a classification percentage of 99.64% and a localization deviation of 0.094 mm. [PERSON] et al. [47] used 550 training images to train two different YOLOv5 networks to classify six different classes of screw heads and achieved a similar mAP of 0.5:0.95 over 0.82 for both network models. They concluded that a YOLOv5-S network worked better than a YOLOv5-m model because it had significant runtime advantages while maintaining similar accuracy. [PERSON] et al. [48] used a YOLOv4 network to detect screws and an EfficientNetv2 to help eliminate detection errors in order to detect three types of screws. [PERSON] et al. [49] presented an approach to detect a hex-head fastener and its rotational orientation using a segmentation neural network. A two-stage detection framework was proposed by [PERSON] et al. [50] to detect screws under non-uniform lighting conditions. In their framework, the first stage is used to extract used and structurally damaged screws, and the second stage is used to filter out false areas based on reflections. [PERSON] et al.