# Software Application for Automatic Detection and Analysis of Biomass in Underwater Videos
## 3 Results  
To verify the application's functionality, underwater videos captured by the Delphis system within the DeepCom project were used. The dataset used is formed by 12 videos (approximately 4 h) acquired at 30 frames per second. The video resolution is 1080 pixels high by 1920 pixels wide. Using the program in evaluation mode, a study was conducted on the effect of different parameters on detection capability. Random fragments of the database were used for this purpose, and automatic detections were compared with user visual detections. Finally, the use of the program in analysis mode on a large number of videos allowed us to detect the limitations of the program and environments/organisms that may lead to detection failures. Although the results obtained are influenced by the type of camera, lighting, and other peculiarities of observations, they provide a good approximation of the potential of this application as well as its limitations. In the reference case, dark noise correction does not seem to be necessary as it does not significantly affect item detection. This is probably because the Atlantic Ocean water acts as a coolant for the capsule containing the camera and cannot be extrapolated to other cameras or recording temperatures. However, homogenizing the background image has proven to be very helpful in improving the program's detection capability. The videos were captured using two lateral spotlights with 3 white LED lights equipped with a power of 1200 lumens each. We have observed that, under these lighting conditions, the threshold for subtracting the background from the image is reduced to a value, in grayscale, of around 15. The program has proven to be highly reliable in the detection of small items, with a detection threshold even better than simple visual inspection. In fact, the software exhibits a higher detection capacity compared to the human eye. To quantify the minimum detectable contrast, visual detection was applied to random images, selecting 20 images in which the program detected more items than the observer (Figure 11). To ensure that the program-detected items are not false positives, only those whose presence in the scene persists for at least 0.2 s (6 consecutive images) are chosen. Using the contour of these items, contrast is calculated on grayscale images as the ratio between the mean illumination value inside  
Figure 10: DeepView analysis mode page. and around the item. The program was able to detect items with contrasts ranging from 0.8 to 1.2 that were not visible to the observers. In order to evaluate the program's reliability (false positive detections), the code was run in automatic mode on a dataset of videos. For this purpose, we have used 10 clips of 1-4 min duration extracted from videos recorded during the DeepCom project. The results exhibit high-frequency errors (Figure 12a) characterized by fluctuations in the number of detections between consecutive images that stem from the numerical noise introduced by the detection process. However, these fluctuation detections do not impact the final program output, where the temporal resolution is reduced (minimum 0.5 s), resulting in the elimination of higher-frequency errors (Figure 12b). Sudden fluctuations in density detection can be easily discerned in temporal analysis (Figure 12b) and can serve as indicators of false positives in the detection process. Through the examination of these instances, we can infer the origin of erroneous detections, in this case the appearance of a tunicate (Figure 13a). In scenarios involving the presence of partially translucent or bioluminescent organisms, the program may erroneously identify a single large organism as a multitude of small items, for example, a siphonophore (Figure 13b). In the reference case, their occurrence in the analyzed videos is sporadic,  
Figure 11: Detected items in an underwater scene by code (green circles) and by human observer (red circles labeled with red numbers). Figure 12: Results for a DeepCom videoclip including a tunicate (subphylum Tunicata).