Towards More Efficient EfficientDets and Real-Time Marine Debris Detection

Federico Zocco, Tzu Chieh Lin, Ching I. Huang, Hsueh Cheng Wang, Mohammad Omar Khyam, Mien Van*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Marine debris is a problem both for the health of marine environments and for the human health since tiny pieces of plastic called 'microplastics' resulting from the debris decomposition over the time are entering the food chain at any levels. For marine debris detection and removal, autonomous underwater vehicles (AUVs) are a potential solution. In this letter, we focus on the efficiency of AUV vision for real-time marine debris detection. First, we improved the efficiency of a class of state-of-the-art object detectors, namely EfficientDets [1], by 1.5% AP on D0, 2.6% AP on D1, 1.2% AP on D2 and 1.3% AP on D3 without increasing the GPU latency (see Fig. 1). Subsequently, we created and made publicly available a dataset for the detection of in-water plastic bags and bottles and trained our improved EfficientDets on this and on two public datasets for marine debris detection. Finally, we began the testing of real-time detection performance on a simulator of marine environments.

Original languageEnglish
Pages (from-to)2134-2141
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number4
StatePublished - 1 Apr 2023


  • data sets for robotic vision
  • Deep learning methods
  • energy and environment-aware automation
  • marine robotics
  • object detection


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