DePondFi’23 Challenge on Real-Time Pond Environment: Methods and Results

Abstract

This article summarizes the Detection of Pond Fish Challenge (DePondfi’23 Challenge), held during the National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG 2023). The main goal of the challenge was to find the most effective methods for detecting pond fish in underwater images, overcoming obstacles such as poor visibility, variations in turbidity, and environmental shifts. Sixty participants registered, with 15 teams submitting results for phase 1. The challenge concluded with four teams earning top honors based on mAP (mean Average Precision) score and time complexity. The mAP scores achieved by toppers are as follows: DETECTRON - 38.93%, DMACS SAI - 36.65%, PondVision - 31.63%, and Sahajeevis - 29.06%. This article describes the toppers method and discusses the detection results. Our challenge event is in line with Sustainable Development Goal 14, which focuses on the conservation and sustainable utilization of ponds and marine resources for sustainable development.

Keywords

• Aquaculture
• underwater challenges
• detection models
• mAP
• computational time