An Enhanced Insect Pest Counter Based on Saliency Map and Improved Non-Maximum Suppression

SIMPLE SUMMARY: Simple Summary: Accurately counting the number of insect pests from digital images captured on yellow sticky traps remains a challenge in the field of insect pest monitoring. This paper develops a new approach to counting the number of insect pests using a saliency map and improved non-maximum suppression. Specifically, a saliency map is exploited to construct a region proposal generator, and a convolutional neural network (CNN) model is used to classify each region proposal as a specific insect pest class, resulting in detection bounding boxes. An improved non-maximum suppression is further developed to sophisticatedly handle the redundant detection bounding boxes, and the insect pest number is thus obtained through counting the handled detection bounding boxes, each of which covers one insect pest. As this insect pest counter may miscount insect pests that are close to each other, the widely used Faster R-CNN is further integrated with the mentioned insect pest counter to construct a dual-path network. Extensive experimental simulations show that the two proposed insect pest counters achieve significant improvements in terms of F1 score against state-of-the-art object detectors as well as insect pest detection methods. ABSTRACT: Accurately counting the number of insect pests from digital images captured on yellow sticky traps remains a challenge in the field of insect pest monitoring. In this study, we develop a new approach to counting the number of insect pests using a saliency map and improved non-maximum suppression. Specifically, as the background of a yellow sticky trap is simple and the insect pest object is small, we exploit a saliency map to construct a region proposal generator including saliency map building, activation region formation, background–foreground classifier, and tune-up boxes involved in region proposal generation. For each region proposal, a convolutional neural network (CNN) model is used to classify it as a specific insect pest class, resulting in detection bounding boxes. By considering the relationship between detection bounding boxes, we thus develop an improved non-maximum suppression to sophisticatedly handle the redundant detection bounding boxes and obtain the insect pest number through counting the handled detection bounding boxes, each of which covers one insect pest. As this insect pest counter may miscount insect pests that are close to each other, we further integrate the widely used Faster R-CNN with the mentioned insect pest counter to construct a dual-path network. Extensive experimental simulations show that the two proposed insect pest counters achieve significant improvement in terms of [Formula: see text] score against the state-of-the-art object detectors as well as insect pest detection methods.