LI-DWT- and PD-FC-MSPCNN-Based Small-Target Localization Method for Floating Garbage on Water Surfaces

Abstract

Typically, the process of visual tracking and position prediction of floating garbage on water surfaces is significantly affected by illumination, water waves, or complex backgrounds, consequently lowering the localization accuracy of small targets. Herein, we propose a small-target localization method based on the neurobiological phenomenon of lateral inhibition (LI), discrete wavelet transform (DWT), and a parameter-designed fire-controlled modified simplified pulse-coupled neural network (PD-FC-MSPCNN) to track water-floating garbage floating. First, a network simulating LI is fused with the DWT to derive a denoising preprocessing algorithm that effectively reduces the interference of image noise and enhances target edge features. Subsequently, a new PD-FC-MSPCNN network is developed to improve the image segmentation accuracy, and an adaptive fine-tuned dynamic threshold magnitude parameter V and auxiliary parameter P are newly designed, while eliminating the link strength parameter. Finally, a multiscale morphological filtering postprocessing algorithm is developed to connect the edge contour breakpoints of segmented targets, smoothen the segmentation results, and improve the localization accuracy. An effective computer vision technology approach is adopted for the accurate localization and intelligent monitoring of water-floating garbage. The experimental results demonstrate that the proposed method outperforms other methods in terms of the overall comprehensive evaluation indexes, suggesting higher accuracy and reliability.