Fish-Pak: Fish species dataset from Pakistan for visual features based classification

Abstract

Fishes are most diverse group of vertebrates with more than 33000 species. These are identified based on several visual characters including their shape, color and head. It is difficult for the common people to directly identify the fish species found in the market. Classifying fish species from images based on visual characteristics using computer vision and machine learning techniques is an interesting problem for the researchers. However, the classifier's performance depends upon quality of image dataset on which it has been trained. An imagery dataset is needed to examine the classification and recognition algorithms. This article exhibits Fish-Pak: an image dataset of 6 different fish species, captured by a single camera from different pools located nearby the Head Qadirabad, Chenab River in Punjab, Pakistan. The dataset Fish-Pak are quite useful to compare various factors of classifiers such as learning rate, momentum and their impact on the overall performance. Convolutional Neural Network (CNN) is one of the most widely used architectures for image classification based on visual features. Six data classes i.e. Ctenopharyngodon idella (Grass carp), Cyprinus carpio (Common carp), Cirrhinus mrigala (Mori), Labeo rohita (Rohu), Hypophthalmichthys molitrix (Silver carp), and Catla (Thala), with a different number of images, have been included in the dataset. Fish species are captured by one camera to ensure the fair environment to all data. Fish-Pak is hosted by the Zoology Lab under the mutual affiliation of the Department of Computer Science and the Department of Zoology, University of Gujrat, Gujrat, Pakistan.

Deep learning for visual recognition and detection of aquatic animals: A review

“…Therefore, LifeCLEF15 dataset is often used to test the robustness of the proposed object detection algorithm in changing environments 60 . Fish‐Pak mainly collected the images of the body, head and scale of six different fish species through the camera to facilitate the DL models to learn clearer features, but the number of images is relatively small 61 . F4K dataset is a real‐time video dataset captured by an underwater surveillance camera in open sea 62 .…”

Automated Freshwater Fish Species Classification using Deep CNN

“…It has been observed that the research on fish identification mostly deals with marine-water fish resources and only a handful of research has been executed on fresh-water fish species. Meanwhile, a few researchers utilized pre-trained CNN models for fresh-water fish image classification where a constant background was maintained for all images captured in a laboratory environment [ 24 – 27 ]. Although these works demonstrate satisfactory performance, further research into the effect of varied backgrounds on the performance of these deep-learning models is still required.…”

“…Although these works demonstrate satisfactory performance, further research into the effect of varied backgrounds on the performance of these deep-learning models is still required. Moreover, there is still scope for improvement in the accuracy of the small-indigenous fish classification problems [ 24 , 28 ]. Indian water bodies carry varieties of fresh-water fish species, still, we are unable to track down any research article focusing related to these species.…”

Measurement of Fish Morphological Features through Image Processing and Deep Learning Techniques

“…Fish classification was also studied in [9], where an image dataset of six different fish species was exhibited (Fish Pack). Convolutional neural networks (CNN) were used, but no further implementation details were presented in [10]. Fish recognition from underwater images poses several challenges, such as poor image quality, unexpected objects, distortion and refraction.…”