Fin Detection Results

The fin detection algorithm was tested on the two data-sets and the results are presented in table4.1.

Data-set 1(MOWI) 2(IMR)

Images in data-set 537 246

Success rate 99.2% 93.5%

Classified with threshold: Otsu 519 232

Threshold: 100 11 8

Threshold: 170 4 1

No pectoral fin detected 2 5

Misclassifications 1 8

Sum of faults 3 13

Table 4.1: The results for testing the fin detection algorithm on both data-sets. The algorithm performs better on dataset 1, but this was the set the algorithm was developed

on. It performs well on the IMR data-set as well.

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C. A. Lende J. N. Lundal 75 It is clear for table4.1that segmenting the images with thresholding using otsu’s method was a good choice. It distinguished the pectoral fin in over 90%(close to 95%) of the cases. The hard thresholding of 100 and 170 picked up some of the few images otsu’s method couldn’t solve and bettered the success rate in both instances.

The method was developed to detect the pectoral fin on data-set 1, but was tested on data-set 2 after finalization. Data-set 2 has a more crude set of images, where the background fluctuates with patterns and other aforementioned disadvantages in table 3.1.

It should be noted that table. 4.1 is not as truthful as it may appear. The results in the ”Threshold”-rows are not completely accurate. The misclassification row are images where the algorithm thought it found the pectoral fin, but other parts of the fish were ac-tually detected. Whether the algorithm classified them wrongly with the otsu-threshold or any of the hard ones, is unknown, but since the otsu-threshold classified many times more images than the hard thresholds, it can be assumed that the misclassifications belong there. Truer values may therefore be: 518 and 224 in row 4.

A detailed explanation of the results is found below for both data-sets.

4.1.1 Results on Data-set 1, MOWI

This algorithm achieved a success rate of 99.2% on 537 images, either misclassifying or not detecting a pectoral fin on three images. In two images, no pectoral fin could be located and one was wrongly classified. The three images are presented in fig. 4.1.

Figure 4.1: The three images who were either misclassified or no pectoral fin could be detected.

Fig. 4.2 shows nine images who were correctly classified with both their nose, tail tips and pectoral fin.

Figure 4.2: Nine random correctly classified images of 537

C. A. Lende J. N. Lundal 77 4.1.2 Results on Data-set 2, IMR

It should be experimented how the developed method worked on a data-set it wasn’t specifically built for. Since it is tailored to data-set 1 from MOWI, its robustness should be tested on images with poorer quality for this project, as mentioned in table. 3.1with data-set 2 from IMR.

The method was modified slightly to fit these images. These images were larger than the data-set 1 images, and thresholds had to be adjusted. In data-set 1 the size-threshold for the pectoral fin was 800, but in data-set 2 most pectoral fins occupied over 20 000 pixels. It led to areas much smaller than the fins being detected, such as the iris of the eye and random areas closer than the fin to the nose.

The method also works together with the previous algorithm which locates the nose and tail tips. For this data-set however, the tail was not in the image. In most cases, it was simply left out and in a few images a human hand held the salmon still. Instead of modifying the method, the nose point was located manually on each image and fed into the fin detection method. Since locating the nose and tail tips wasn’t the objective, this was a good substitute.

Even on cruder images, the method performed with a 93.5% success rate. There was a total sum of 13 faults, either misclassifications or no fin was detected, in the data-set.

Fig. 4.3shows four instances where the algorithm either misclassified images or detected no pectoral fin at all.

Figure 4.3: Four images out of thirteen faulty images. The other wrongly classified images were similar instances like these

The images in fig. 4.3often have clear possible reasons for why they were misclassified.

The fish in the leftmost images are greener compared to most other salmons. The pectoral fin is a darker green, but still green. Compared to the fish in data-set 1, where the pectoral fin is distinct(dark to light skin), the fin is harder to separate when it has a green hue similar to its skin. The rightmost images’ fish has either a small pectoral

fin or one of normal size, but they are both very light in intensity, much like the skin of the rest of the fish. This makes it harder to separate it from the body.

4.1.2.1 Potential Improvements

To make the method more efficient on images it wasn’t constructed to work on, instead of only having a lower threshold for the fin size, and upper could be added. Then large areas like the rightmost images in fig. 4.3would be eliminated. With an accurate bracket for the pectoral fin, an adaptive threshold method could be added. The method could try out different thresholds(with increments of, for example, 10) until a suitable area is found close enough to the nose.