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Image segmentation is a concept that is often used for object detection. Thisdetection has difficulty detecting objects with backgrounds that have manycolors and even have a color similar to the object being detected. This studyaims to detect fish using segmentation, namely segmenting fish images usingk-means clustering. The segmentation process is processed by improving theimage first. The initial process is preprocessing to improve the image.Preprocessing is done twice, before segmentation using k-means and after.Preprocessing stage 1 using resize and reshape. Whereas after k-means is thecontrast-limited adaptive histogram equalization. Preprocessing results aresegmented using k-means clustering. The K-means concept classifies images usingsegments between the object and the background (using k = 8). The final step isthe morphological process with open and close operations to obtain fishcontours using black and white images based on grayscale images from colorimages. Based on the experimental results, the process can run well, with thessim value close to 1, which means that image information does not change.Processed objects provide a clear picture of fish objects so that this k-meanssegmentation can help detect fish objects.

Fish Detection Using Morphological Approach Based-on K-Means Segmentation