## 1 Introduction  
According to the World Health Organization (WHO), complications in inpatient surgical operations occur for at most 25% of the patients, and at least half of the cases in which surgery led to harm or damage are considered preventable [1]. This means that improvement of surgical performance can lead to better outcomes of surgical operations. Surgical performance can be improved through sophisticated remote manipulation of the robot [2; 3; 4], but surgical feedback also has a positive effect on surgical performance [5]. While manual evaluation methods such as the objective structured assessment of technical skills (OSATS), and the global operative assessment of laparoscopic skills (GOALS) can assess the surgical skills and are beneficial in terms of their improvements, it is both time and labor consuming, because surgeries could last multiple hours [6; 7]. Manual assessment is subjective to observer bias and can lead to subjective outcomes [8]. Detection of surgical instruments is one of the indicators used for the analysis of surgical operations and it can be useful for the effective and objective analysis of surgery [9]. This also helps prevent surgical tool collision by informing the operator during the procedure [10].  
Various approaches have been published on surgical tool detection. [PERSON] et al. [11] imaged markers, which were placed on surgical instruments with the use of two infrared cameras. [PERSON] et al. [12] presented an approach based on radiofrequency identification (RFID) for real-time tracking of laparoscopic instruments. However, at present, there is no proper and reliable antenna system for routine intraoperative applications [12]. However, detection tools utilizing markers interfere with the surgical workflow and require modifications of the tracked instrument [13].  
Efforts have been expended to develop vision-based and marker-less surgical tool detection using feature representations, based on color [14; 15], gradients [16], or texture [17]. [PERSON] et al. [18] segmented the instruments in images and recognized their types based on three-dimensional models. Many researchers have also addressed surgical tool detection with the use of convolutional neural networks. [PERSON] et al. [19] proposed for the first time the use of a CNN for multiple recognition tasks on laparoscopic videos. Several works [20; 21; 22] of surgical tool detection by CNNs have been proposed as a part of the M2 CAI 2016 tool presence detection challenge [23]. [PERSON] et al. [24] performed surgical tool localization and phase recognition in cholecystectomy videos based on faster R-CNN. [PERSON] [25] proposed a new method to detect and to identify surgical tools by calculating a bounding box using a random forest algorithm, and then, extracting multiple features from each bounding box. [PERSON] et al. [26] introduced a method of robotic instrument semantic segmentation based on deep learning, in both binary and multiclass settings. However, these studies dealt with tool detection in a frame-wise manner, but did not employ time information, and did not detect tools in real time.  
In this study, we address the issue of tool detection in laparoscopic surgical videos. Our method is faster and more accurate than cutting-edge technologies [20; 21; 22; 24], and it can be applied during surgery or real-time analyses. We propose a new method to detect the surgical tool in laparoscopic images using YOLO9000 [27] and detect missing surgical tools based on motion vector prediction.