Streamlining Deep Learning Network for Real-time Sea Turtle Detection

Muhamad Dwisnanto Putro (Sam Ratulangi University)
Yuliana Mose (Jurusan Teknik komputer, Fakultas IPTEk dan Keguruan, Universitas Trinita)
Alex Copernikus Andaria (Jurusan Teknik komputer, Fakultas IPTEk dan Keguruan, Universitas Trinita)
Jane Litouw (Jurusan Teknik Elektro, Fakultas Teknik, Universitas Sam Ratulangi)
Vecky Canisius Poekoel (Jurusan Teknik Elektro, Fakultas Teknik, Universitas Sam Ratulangi)
Xaverius Najoan (Jurusan Teknik Elektro, Fakultas Teknik, Universitas Sam Ratulangi)

Jurnal Rekayasa elektrika
Vol 20, No 3 (2024)

Monitoring turtle behavior is a conservation effort to preserve its habitat, and the detection process is a vital initial stage. On the other hand, robotics demands a deep learning network to automatically detect the presence of sea turtles that can operate in real-time. The need for increased model speed in the inference stage has led to many lightweight vision-based detectors. This work proposes a novel turtle detection to localize multiple sea turtles using a deep learning method. A lightweight primary extractor is applied to distinguish crucial features without producing a huge computational. An excited group attention is offered as an enhancement module that can capture essential turtle components in multi-level convolutional patches. A new turtle dataset is proposed that contains lighting, blur, occlusion, and complex background challenges. The evaluation results show that the proposed model performs higher accuracy than other lightweight object detection models. High-efficiency benefits models that can be implemented on low-end devices in terms of real-time data processing speed.