Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks.

Laboratory Observations Regarding Different Instars of Cyclosainsulana(Costa, 1834)(Araneidae ) During Developmental Stages

The current experiment was conducted to find out the optimal conditions for mass rearing and developmental changes of Cyclosainsulana. The lab. conditions were maintained at （27±2）℃ and （65±5）% RH. The clear perplex cages and natural diet consisting of the aphids, larvae of the house fly and larvae of drosophila were used for rearing. C. insulana took （123.12±7.26） days to develop from eggs to adults passing through eight instars under prevailing vivo conditions. The eggs were greenish white in color with average size of 0.57 mm ±0.034. The eggs spent （7.52±1.64） days in emergence. Maximum number of spiderlings survived at the 5th instar （84%） and minimum at the 1st instar （34%）. The measurements of different body parts including the total body length, cephalothorax and pedipalps of the both male and female C. insulana were done with the help of micrometer and presented as mean± SD. The measurements varied in the each developing instar. It was concluded that spiders were difficult to rear in the lab. conditions and each developing stage which was regarded as instars showed variations in size colors and body characteristics.