Arthropod Taxonomy Orders Object Detection in ArTaxOr dataset using YOLOX

Abstract

Abstract The detection and classification of insect species represent challenging computer vision tasks that have significant applications in zoology and agriculture. Fortunately, biologists and taxonomists have developed a systematic approach to organizing organisms, which results in a hierarchical classification system. Insect classification employs a hierarchical structure that includes object detection at the order level, family classification, and species classification. However, the conventional insect identification method is time-consuming and requires the expertise of highly skilled taxonomists to identify insects accurately based on morphological characteristics. This paper presents a pioneering study on the automatic detection and classification of Arthropod Taxonomy Orders using an enhanced variant of the You Only Look Once (YOLOX) framework along with the Arthropod Taxonomy Orders Object Detection (ArTaxOr) Dataset. The proposed ArTaxOr dataset encompasses diverse arthropod species such as insects, spiders, crustaceans, centipedes, millipedes, and isopods. Moreover, some images within this dataset depict multiple species with varying sizes, shapes, and colors. Accordingly, all images are resized to 640 $$\times$$ × 640 to ensure compliance with the requisite input image size for YOLOX. Further, mosaic augmentation is employed to enhance the model’s accuracy in recognizing small objects. Inspite of natural complexity in a majority of dataset images, the proposed YOLOX-based model attained superior mean average precision. The outcomes of this study could act as a standard against which forthcoming research in this domain could be compared or judged.

Keywords

• Arthropods
• Mosaic augmentation
• YOLOX
• Insect classification
• Roboflow
• Object detection