# A lightweight network model designed for alligator gar detection

## TL;DR

A new lightweight detection model called ARD-Net was developed to monitor alligator gar in wild waters more efficiently.

## Contribution

ARD-Net introduces a cross-domain grid matching strategy and a novel feature extractor for real-time alligator gar detection.

## Key findings

- ARD-Net achieves detection speed 1.48 times faster than YOLOv5 with similar accuracy.
- The model outperforms YOLOv8 in detection efficiency and model size.
- A new dataset of alligator gar images was created for training and testing.

## Abstract

When using advanced detection algorithms to monitor alligator gar in real-time in wild waters, the efficiency of existing detection algorithms is subject to certain limitations due to turbid water quality, poor underwater lighting conditions, and obstruction by other objects. In order to solve this problem, we developed a lightweight real-time detection network model called ARD-Net, from the perspective of reducing the amount of calculation and obtaining more feature map patterns. We introduced a cross-domain grid matching strategy to accelerate network convergence, and combined the involution operator and dual-channel attention mechanism to build a more lightweight feature extractor and multi-scale detection reasoning network module to enhance the network’s response to different semantics. Compared with the yoloV5 baseline model, our method performs equivalently in terms of detection accuracy, but the model is smaller, the detection speed is increased by 1.48 times, When compared with the latest State-of-the-Art (SOTA) method, YOLOv8, our method demonstrates clear advantages in both detection efficiency and model size,and has good real-time performance. Additionally, we created a dataset of alligator gar images for training.