DANet Drone Coastline Inspection for Floating Garbage Route Correction
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Solution Overview
Problem
Conventional patrol and inspection methods for coastline floating garbage are inefficient and costly due to manual labor requirements, limited visual range, and the difficulty in accessing long and tortuous coastlines, with existing automatic drone systems relying on low-robust dynamic binarization methods that are prone to deviations from the ideal route.
Innovation Solution
A DANet-based drone patrol and inspection system that includes an image acquisition module, feature extraction module, network training module, and path correction module to automatically recognize coastline and floating garbage, adjust drone flight directions, and optimize routes using panoramic segmentation and attention modules for precise segmentation and efficient path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual patrol and inspection methods are used, then human operators can visually identify floating garbage, but the patrol cycle is long, visual range is limited, and a lot of manpower and material resources are consumed
Solution Approach 1:
The patent replaces manual mechanical patrol with an automated drone system equipped with cameras and computer vision algorithms. The drone automatically captures images of the coastline and uses image processing algorithms to detect floating garbage, eliminating the need for human operators to physically patrol and visually identify pollutants, thereby significantly improving patrol efficiency while maintaining detection accuracy
Solution Approach 2:
The system enables self-service detection through automated image capture and processing. The drone autonomously navigates along the coastline, captures images, and the embedded algorithms automatically detect and classify floating garbage without requiring manual intervention at each detection point, allowing continuous operation and reducing resource consumption
2Reliability
If on-site monitoring devices are deployed, then real-time monitoring is possible, but arrangement costs are high and the entire basin cannot be covered due to limited shooting range
Solution Approach 1:
The patent employs a multi-functional drone system that combines navigation, image capture, garbage detection, and route optimization in a single platform. This universal device replaces multiple separate monitoring systems, reducing overall system complexity while maintaining comprehensive coverage and reliable detection across the entire coastline basin
Solution Approach 2:
The system transitions from two-dimensional ground-based monitoring to three-dimensional aerial monitoring. The drone operates in the air space above the coastline, providing a bird's-eye view that overcomes the limited shooting range of ground-based devices and enables coverage of the entire basin including difficult-to-access areas like cliffs and rocky shores
3Extent of automation
If dynamic binarization detection method is used for automatic drone patrol, then automatic flight path determination is achieved, but the method has low robustness and the drone easily deviates from the ideal route due to changeable coastal conditions
Solution Approach 1:
The patent dynamically adjusts detection parameters such as threshold values and processing algorithms based on real-time coastal conditions. The system monitors environmental variables and modifies its detection parameters adaptively, maintaining high route accuracy and detection reliability even when coastal conditions change, thereby improving the robustness of the automated patrol system
Solution Approach 2:
The system implements feedback mechanisms where detection results and positional data are continuously fed back to the control system. This feedback loop allows real-time correction of flight path deviations and adjustment of detection parameters, ensuring the drone maintains accurate routing and reliable detection despite variations in coastal conditions
Data Source
AI summary
A double attention network (DANet)-based drone patrol and inspection system for coastline floating garbage, including: an image acquisition module configured to shoot a video of a coastline in need of patrol and inspection by using a drone, and obtain an image from the video; a feature extraction module configured to extract shallow features and deep features, fuse the shallow features and the deep features to obtain a shared feature, and finally output a panoramic recognition result; a network training module configured to perform training on the labeled image so that the network can recognize the coastline and floating garbage; and a path correction module configured to adjust a flying direction of the drone.