## 2 Methods
### Slip Detection
#### 2.3.3 Optical Path Occlusion and Remaining Background Noise
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objects** & **All** & **Textured** & **Untextured** \\ \hline Accuracy & 82.38\% & 87.19\% & 67.67\% \\ Precision & 87.99\% & 83.13\% & 76.77\% \\ Recall & 75.00\% & 93.33\% & 50.67\% \\ F1 Score & 80.98\% & 87.93\% & 61.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance when optimized on Accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objects** & **All** & **Textured** & **Untextured** \\ \hline Accuracy & 78.81\% & 81.23\% & 66.33\% \\ Precision & 74.69\% & 74.45\% & 67.38\% \\ Recall & 87.14\% & 95.09\% & 63.33\% \\ F1 Score & 80.44\% & 83.51\% & 65.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance with a weighted optimization on Recall and Accuracy at a 2:1 ratio objects with smooth surfaces and no printed texture do not show any change in pixel value when they move relative to the camera. Although a motion field exists on the object's surface, it cannot be detected visually. This makes it challenging to detect slip events solely through visual information, especially if there is no background or gripper to relate the motion to. This distinguishes the proposed method from others, as it applies background removal for increased robustness, but it may sacrifice some information about relative motion to the background. Secondly, transparent objects present an additional challenge. They not only lack a detectable motion field even though one exists but also allow the background of the scene to be visible through the object itself. This causes false positives to be detected when the background moves due to environmental movements or robot arm motions. The low recall of 50.67% for untextured and transparent objects indicates a significant number of false negatives in the results. In conclusion, the proposed algorithm offers a viable approach for slip detection with minimal sensory data and is effective in many scenarios. However, further improvements may be necessary to address challenges related to objects with smooth surfaces and transparent objects to enhance the algorithm's overall performance and reliability. Generally, the algorithm appears to have an easier time detecting slips than detecting nonslip cases. This can be observed in the recall and accuracy of both optimization approaches (see Tables 1 and 2). The recall is significantly higher than the accuracy, indicating that false positives occur more frequently than false negatives. In the context of the application, this is generally acceptable, as a false positive would only require an adjustment or tightening of the grasp, whereas a false negative could result in dropping the object. By optimizing the algorithm for higher recall, false negative cases can be minimized without sacrificing too much accuracy. Since the works of the research groups we are comparing our proposed method with all use learning methods that process image data but are trained in a controlled environment [8; 9; 11; 12; 13], it is possible that the proposed method performs better in uncontrolled environments. The performance of DNNs or convolutional neural networks (CNNs) that are trained on images with a consistent white background and tested in the same environment might be affected by a changing background. For industrial applications, this limitation might not be relevant, but for using the robot as an assistive device in daily life, the ability to perform regardless of the environment is essential. Our work proposes the first method tailored to this specific use case in assistive robots. Despite the limitations of our proposed method, it still performs at a comparable level to the state of the art, regardless of the environment (see Table 3). The dataset we used to evaluate the performance of our algorithm is not perfect, as the camera setup differed from ours. In our setup, the camera was attached to the end effector, but in the dataset, it was stationary and placed in front of the setup.