Vehicle color recognition based on smooth modulation neural network with multi-scale feature fusion

Vehicle Color Recognition(VCR)plays a vital role in intelligent traffic management and criminal investigation assistance.However,the existing vehicle color datasets only cover 13 classes,which can not meet the current actual demand.Besides,although lots of efforts are devoted to VCR,they suffer from the problem of class imbalance in datasets.To address these challenges,in this paper,we propose a novel VCR method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion(SMNN-MSFF).Specifically,to construct the benchmark of model training and evaluation,we first present a new VCR dataset with 24 vehicle classes,Vehicle Color-24,consisting of 10091 vehicle images from a 100-hour urban road surveillance video.Then,to tackle the problem of long-tail distribution and improve the recognition performance,we propose the SMNN-MSFF model with multiscale feature fusion and smooth modulation.The former aims to extract feature information from local to global,and the latter could increase the loss of the images of tail class instances for training with class-imbalance.Finally,comprehensive experimental evaluation on Vehicle Color-24 and previously three representative datasets demonstrate that our proposed SMNN-MSFF outperformed state-of-the-art VCR methods.And extensive ablation studies also demonstrate that each module of our method is effective,especially,the smooth modulation efficiently help feature learning of the minority or tail classes.Vehicle Color-24 and the code of SMNN-MSFF are publicly available and can contact the author to obtain.

On-Line Defect Detection of Aluminum Coating Using Fiber Optic Sensor

Aluminum metallization using the sprayed coating for exhaust mild steel （MS） pipes of tractors is a standard practice for avoiding rusting. Patches of thin metal coats are prone to rusting and are thus considered as defects in the surface coating. This paper reports a novel configuration of the fiber optic sensor for on-line checking the aluminum metaUization uniformity and hence for defect detection. An optimally chosen high bright 440 nm BLUE LED （light-emitting diode） launches light into a transmitting fiber inclined at the angle of 60° to the surface under inspection placed adequately. The reflected light is transported by a receiving fiber to a blue enhanced photo detector. The metallization thickness on the coated surface results in visually observable variation in the gray shades. The coated pipe is spirally inspected by a combination of linear and rotary motions. The sensor output is the signal conditioned and monitored with RISHUBH DAS. Experimental results show the good repeatability in the defect detection and coating non-uniformity measurement.