## 4 Discussion and Conclusions  
In this paper, we proposed the new method of detecting and classifying surgical instruments in laparoscopic images. This method has two main advantages--it can be used during real-time operations, and it is robust in comparison to the existing methods. Firstly, the proposed method can detect surgical tools in real time by using the object detection system YOLO9000. Unlike other methods, You Only Look Once (YOLO) does not allow for finding the region of interest (ROI). Conventional methods aim to identify the ROI from an input image and thereafter, to classify each ROI. However, applying YOLO allowed for the diminishing of the time required to calculate the ROI. YOLO divides an input image into a set of grid cells and then, performs classification of each grid cell. Owing to this key feature of YOLO, the proposed algorithm can detect surgical tools in real time (Table 3). Moreover, the proposed method is deemed to be robust. In other words, the proposed method demonstrates the uniform and excellent performance in the detection of surgical instruments of all classes. Based on the results provided in Table 2 and Figure 4, it can be concluded that in comparison to other algorithms, the proposed method has a uniform mean average precision (mAP)--over 80--for all classes of surgical instruments, and the highest average mAP with respect to all considered surgical tools. As shown in Figure 4, while the performance of other algorithms with the similar mAP deteriorates for certain classes, the performance of the proposed algorithm is plotted as a flat graph, which confirms its high robustness. Achieving the robustness of the proposed algorithm is possible owing to the use of the upgraded version of YOLO--YOLO9000. As mentioned earlier, YOLO has a high processing speed, as grid cells are considered instead of ROI. However, it has the problem of lacking accuracy in the first version of YOLO. This can be seen by comparing the performance results of [34] and [24] in Table 2. The study [34] is dedicated to the detection of surgical instruments using the early version of YOLO, and [24] is a study in which surgical instruments were detected applying the faster R-CNN, a typical algorithm using ROI. The results presented in Table 2 and Figure 4 show that the performance of the early version of YOLO is lower than that of the approach based on ROI identification. YOLO9000 has come out to solve these problems. As shown in Figure 2, compared to the earlier version of YOLO, YOLO9000 has subdivided the input image into smaller grid cells resulting in more sophisticated detection. Another reason for the robustness of the proposed algorithm is that it enables improvements to the detection performance of successive surgical tools owing to the prediction of missing tools. Missing tool detection leads to better performance, as it enables the redetection of surgical tools that have been present in the previous frame, but are not detected in the current frame. As YOLO9000 uniformly divides the input image into grid cells, detection performance may deteriorate when the  
Figure 8: Example of errors in surgical tool detection using YOLO9000 and missing tool detection **(a)** The specimenbag was detected incorrectly in the previous frame, **(b)** The incorrectly detected specimenbag is judged as the missing tool, hence, it is redetected by applying the missing tool detection algorithm in the current frame. main feature is located at the boundary of the grid cell. This situation can occur, for example, as the surgical tool moves. Therefore, it is possible to improve the detection performance by predicting the motion trajectory of the surgical tool and adjusting the position of the grid cell correspondingly. Figure 7 shows the difference in error estimates depending on the presence or absence of the missing tool detection algorithm. The results provided in Table 3 also demonstrate the improved performance owing to this algorithm.