ASP-Det: Toward Appearance-Similar Light-Trap Agricultural Pest Detection and Recognition - Wang, Fenmei; Liu, Liu; Dong, Shifeng; Wu, Suqin; Huang, Ziliang; Hu, Haiying; Du, Jianming (Frontiers in Plant Science, 2022)
Automatic pest detection and recognition using computer vision techniques are a hot topic in modern intelligentagriculture, but suffer from a serious challenge: difficulty distinguishing the targets of similar pests in 2D images.The appearance-similarity problem could be summarized into two aspects: texture similarity and scale similarity.In this paper, we re-consider the pest similarity problem and state a new task for the specific agriculturalpest detection, namelyAppearanceSimilarityPestDetection (ASPD) task. Specifically, we propose twonovel metrics to quantitatively define the texture-similarity and scale-similarity problems, in which the formeris measured by Multi-Texton Histogram (MTH) and the latter is measured by Object Relative Size (ORS).Following the new definition of ASPD, we build a task-specific dataset, named PestNet-AS that is collectedand re-annotated from PestNet dataset, and also a corresponding method ASP-Det. In detail, our ASP-Det isdesigned to solve the texture-similarity by proposing a Pairwise Self-Attention (PSA) mechanism as well as Non-Local Modules to construct a domain adaptive balanced feature module that could provide high-quality featuredescriptors for accurate pest classification. On the other hand, we also present a Skip-Calibrated Convolution(SCC) module that can balance the scale variation among the pest objects and re-calibrate the feature mapsinto the sizing equivalent of pests. Finally, ASP-Det integrates the PSA-Non Local and SCC modules into a one-stage anchor-free detection framework with a center-ness localization mechanism. Experiments on PestNet-ASshow that our ASP-Det could serve as a strong baseline for the ASPD task.