Quantification of the escapement rate of unwanted fish through a selective device is usually based on catch comparison. This study proposes a new, time efficient method to automatically compare two selective devices by automated counting of fish escapements through each selective device based on video sequences. First, sea trials were conducted to record video sequences of fish escaping a white and black square mesh panel. Then, all of the underwater sequences were automatically analysed by a computer vision software for automated object detection and tracking. Finally, the algorithm was assessed using 150 min of video sequences analysed by humans. We observed that the variability in escapements rate between all the observers on reference video sequences could reach 5%. As the difference in escapements rate between the algorithm and the observers was lower than the variability between observers, the automated approach was validated. The software detected a significant difference in fish escapement rate according to the net colour in the camera field of view: 60% of all fish escaped through the white panel. Our results suggest that net colour influences the escape rates of fish. The colour of the selective device should therefore be investigated further with the aim of increasing their efficiency. Further development of the software could be done to identify species and size of the fish and assess the effectiveness of a selective device by species and size.