Depth estimation from single image using machine learning techniques

Chahal, Nidhi; Pippal, Meghna; Chaudhury, Santanu

doi:10.1145/3009977.3010019

Cited by 3 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Saxena et al [14]have published a novel approach to construct a 3D depth model from a single image. Chahal et al [15] have proposed combining machine learning techniques to generate a depth image from a single image. However, the depth image can only indicate whether object A is closer to or farther from the camera than object B.…”

Section: The Route Planning For the Agriculture Drone For Spraying Pesticidementioning

confidence: 99%

Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying

et al. 2021

View full text Add to dashboard Cite

Tessaratoma papillosa (Drury) first invaded Taiwan in 2009. Every year, T. papillosa causes severe damage to the longan crops. Novel applications for edge intelligence are applied in this study to establish an intelligent pest recognition system to manage this pest problem. We used a detecting drone to photograph the pest and employed a Tiny-YOLOv3 neural network model built on an Embedded system NVIDIA Jetson TX2 to recognize T. papillosa in the orchard to determine the position of the pests in realtime. The pests' positions are then used to plan the optimal pesticide spraying route for the agricultural drone. Apart from planning the optimized spraying of pesticide for the spraying drone, the TX2 embedded platform also transmits the position and generation of pests to the cloud to record and analyze the growth of longan with a computer or mobile device. This study enables farmers to understand the pest distribution and take appropriate precautions in real-time. The agricultural drone sprays pesticides only where needed, which reduces pesticide use, decreases damage to the environment, and increases crop yield.

show abstract

Section: The Route Planning For the Agriculture Drone For Spraying Pesticidementioning

confidence: 99%

Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying

et al. 2021

View full text Add to dashboard Cite

show abstract

“…A first approach involves applying supervised learning to a depth-image dataset, as presented by Saxena et al [15]. More recently, Chahal, Pippal et al [16] propose the use of a combination of machine learning techniques to generate a depth image from a single image. Albeit the latter displaying promising results, no quantification is made as to how long it takes to process one image.…”

Section: B Distance Estimationmentioning

confidence: 99%

Real-Time Hazard Symbol Detection and Localization Using UAV Imagery

Tijtgat¹,

Volckaert

Turck

2017

2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)

View full text Add to dashboard Cite

Abstract-Unmanned Aerial Vehicle (UAV) technology is advancing at a fast pace following the strong rise in interest in its applications for a wide variety of scenarios. One of the promising use cases for UAVs is their deployment during emergency and rescue operations. Their high mobility, aerial viewpoint and flexibility to be operated autonomously are huge assets during crises. UAVs exist in a wide range when it comes to cost, and so do the sensors or accessories they can carry along as payload. During emergencies it may be beneficial to have several low-cost UAVs on site, as opposed to one highly sophisticated, fully-equipped aerial platform. This way more ground can be covered in a shorter span of time during search-and-rescue operations. The situation where a single UAV is on the ground to recharge its battery can also be avoided. In this paper, we present an architecture that identifies and locates objects of interest in real-time using low-cost hardware and a state of the art object detection algorithm. We avoid the use of expensive LIDAR sensors and UAVs, but still manage to determine the position of specific predefined objects in the field with high precision. Without augmenting our detection setup by using intelligent adaptive flight patterns, we can locate an object within 1.5m of its actual location at very low latency. Our detection chain delivers the result and its location in well under one second.

show abstract

Distance Measurement Method Based on Re-blurring with B-spline Function

Wei

Hou

2021

2021 IEEE International Conference on Robotics and Biomimetics (ROBIO)

View full text Add to dashboard Cite

Depth estimation from single image using machine learning techniques

Cited by 3 publications

References 19 publications

Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying

Identification of Fruit Tree Pests With Deep Learning on Embedded Drone to Achieve Accurate Pesticide Spraying

Real-Time Hazard Symbol Detection and Localization Using UAV Imagery

Distance Measurement Method Based on Re-blurring with B-spline Function

Contact Info

Product

Resources

About