Locust plagues are very harmful for food security, quality and quantity of agricultural products. With this consideration, precise locust detection is significant for preventing locust plagues. To achieve this task, aggregate channel feature (ACF) object detector with parameters optimization is applied to detect locusts. Experiment results show that ACF object detector with optimized parameters can achieve 0.39 for average precision and 0.86 for log-average miss rate. Moreover, ACF is a non-deep method using a simple model to detect objects. That is, the proposed method is promising to be embedded in a real-time locust detection system.
|Title of host publication||UK-RAS19 Conference|
|Subtitle of host publication||‘Embedded Intelligence: Enabling & Supporting RAS Technologies’ PROCEEDINGS|
|Place of Publication||Leicester, UK|
|Number of pages||4|
|Publication status||Published - 24 Jan 2019|
|Event||'Embedded Intelligence' UK-RAS19 Conference - Loughborough University, Loughborough, United Kingdom|
Duration: 24 Jan 2019 → 24 Jan 2019
|Conference||'Embedded Intelligence' UK-RAS19 Conference|
|Period||24/01/19 → 24/01/19|
Bibliographical noteThis work was supported by the U.K. Science and Technology Facilities
Council under Grant ST/N006852/1, ST/N006712/1, and ST/N006836/1.