## 3 Experimental Results and Error Analysis  
### Experimental Conditions and Results  
We performed experiments on Ubuntu 16.04 using a GPU NVIDIA GeForce GTX 1080, with 16 GB of memory, and a CPU Intel core i7-4770K. The training dataset was created with the use of vertical flip, horizontal flip, or both, to generate the 1 st to the 7 th videos at m2 cai16-tool-locations, thus resulting in 7492 images in total (Figure 3). In addition, the 10 th video of m2 cai16-tool-locations was used as the validation set. Regarding the test set, the 8 th and 9 th videos from m2 cai16-tool-locations [31] and the videos 11-15 of the m2 cai16-tool dataset [32] were used. The number of each class and the total number of images included in training and test videos are shown in Table 1.  
Furthermore, in the proposed method, the anchor size of the bounding box uses five pairs of values obtained from the size of the ground-truth box of the training set based on k-means clustering. The value pairs of the five boxes are (15.84, 20.17), (5.37, 7.36), (8.09, 8.98), (8.48, 12.10), and (12.08, 13.37). The weight used in training was pre-trained using visual object classes (VOC), and nonmaximal suppression (NMS) [28] was applied. We compared the performance of the proposed method with results presented in other studies conducting experiments on the same dataset. Table 2 and Figure 4 show the performance estimates for our proposed algorithm, for the winner of the 2016 M2 CAI Tool Presence Detection Challenge, and for the algorithm based on the Faster R-CNN [33]. We also compared the performance of the proposed method--the algorithm using the second version of YOLO and motion vector prediction--with the results obtained in our previous work [34] for the algorithm using the first version of YOLO. Moreover, we performed the comparison of the proposed algorithm with the deformable part models (DPM) [35] and EndoNet [19], which used different datasets to detect surgical tools. The performance comparison was conducted based on the mAP estimates [24]. As shown in Table 2, the proposed method has a higher mAP than the alternative algorithms including the winners of the M2 CAI Tool Presence Detection Challenge. This observation was obtained based on the average of all considered tools. Figure 4 shows the mAP values for each class of algorithms, except for the Raju study. The proposed algorithm showed lower performance than some algorithms for such surgical instruments as hook and clipper, but the mAP of all classes was over 80, showing uniform performance regardless of class. Table 3 compares the speed of the proposed algorithm against that of three different algorithms--two algorithms with high performance according to the results provided in Table 2, and an algorithm using random forests [25]. Algorithms using random forests automatically generate bounding boxes and determine the instrument type of the bounding box. The speed comparison is \begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c|c} \hline \hline
**Instrument Type** & **Raju** & **Sahu** & **Twin** & **DPM** & **EndoNet** & **A.Jin** & **B.Choi** & **Proposed Algorithm** \\ \hline Grasper & NA & 73.9 & 82.2 & 82.3 & 84.8 & 87.2 & 89.3 & 92.1 \\ \hline Bipolar & NA & 40.8 & 50.3 & 60.6 & 86.9 & 75.1 & 32.4 & 82.3 \\ \hline Hook & NA & 95.1 & 89.4 & 93.4 & 95.6 & 95.3 & 93.2 & 85.9 \\ \hline Scissors & NA & 26.2 & 17.0 & 23.4 & 58.6 & 70.8 & 66.6 & 81.2 \\ \hline Clipper & NA & 35.3 & 43.6 & 68.4 & 80.1 & 88.4 & 90.3 & 85.3 \\ \hline Irrigator & NA & 33.2 & 12.5 & 40.5 & 74.4 & 73.5 & 42.4 & 82.9 \\ \hline SpecimenBag & NA & 76.6 & 72.2 & 40.0 & 86.8 & 82.1 & 91.4 & 83.2 \\ \hline All tools & 63.8 & 54.5 & 52.5 & 58.4 & 81.0 & 81.8 & 72.26 & 84.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison performance using mAP. Figure 4: Comparison of mean average precision (mAP) estimates by class for all considered algorithms outlined in Table 2, except the Raju method. based on frames per second (FPS) and allows estimating the accuracy of each algorithm.