This work is devoted to the study of methods for detecting retroreflective objects (RRO), including optical and optoelectronic observation devices, based on the search for spatial anomalies in images formed in pulse laser location systems. Algorithms and software for detecting RRO have been developed. At the same time, special attention is paid to various methods of forming diference frames with periodic illumination, including preliminary replacing each pixel of the background images with the maximum value for the corresponding neighborhood. The efectiveness of the proposed methods for detecting retroreflective objects in conditions of intense sunlight, despite the presence of specular and difuse reflecting surfaces in the field of view, is demonstrated.
Recently, much attention has been paid to a special class of optoelectronic laser location systems designed to detect retroreflective objects (RRO), such as photo and video equipment, optical and optoelectronic surveillance systems, various types of reflectors, etc. [1].
The operation of such systems is based on the use of the efect of retroreflection, which occurs when highlighting objects of these types, as a result of which in the field of view in the coordinates corresponding to the RRO, luminous points are formed against the background of the underlying surface, observed only from a position close to the position of the emitter. To increase the eficiency of the detection of RRO at long distances, the organization of synchronous operation of the laser emitter used to illuminate the scene and the photodetector with a highspeed shutter is carried out. Moreover, the illumination is carried out in short pulses, and the photodetector shutter opens for a time close to their duration with a delay (relative to the operation of the emitter) equal to the time of light propagation to the observed objects and back. Due to this, the photodetector perceives the radiation reflected from the objects of interest, and cuts of the light reflected from objects that are closer and further than the specified distance.
As a shutter in devices operating on the basis of the described method, as a rule, a high-speed image intensifier is used. A detailed overview of such devices is provided in [ 2]. However, the use of image intensifier leads to a complication of their design and a decrease in the spatial resolution of the generated images of the controlled scene. An alternative solution is to implement the shutter function using a special algorithm for controlling a two-section photodetector receiver with a lowercase transfer [3]. This approach is implemented in the hardware and software complex [4], the purpose of which is to check the performance of CCD arrays as part of active-pulse devices for detecting RRO without using an image intensifier tube or other external high-speed shutter in its design.
To efectively solve the problem of detecting and recognizing RRO in real conditions, it is necessary to equip laser pulse location systems with built-in functions for processing recorded images, aimed at identifying fragments that potentially contain objects of interest. The processing performed in automatic mode is based on the analysis of the spatial distribution of the image brightness by a sliding window and the assessment of the presence of detectable objects at various points of the observed scene. At the same time, this assessment is traditionally performed by determining the correspondence between the analyzed fragments and the a priori description of the detected objects [5]. However, as practice has shown, this approach is not efective enough due to possible changes in illumination, low resolution of RRO images at a considerable distance and unknown angles of the observed objects, the presence of significant atmospheric distortions and a diverse dynamic background. Therefore, in this paper, to detect RRO in the field of observation, it is proposed to use a method based on the formation of a description of the background for each scene, followed by the search for fragments whose parameters do not correspond to the received description. This approach in the literature is called the search for anomalies [6], does not require the implementation of preliminary training procedures and allows you to detect in the field of view of the observation system atypical fragments in certain parameters on a non-uniform background.
The aim of this work is to develop and experimentally study the efectiveness of digital data processing methods for pulse laser location systems designed for automatic detection of RRO by accumulating signals with a pulse illumination frequency and intra- and inter-frame processing of recorded images.
An optoelectronic stand consisting of sensing channels and synchronous image recording was used for experimental investigation of the RRO detection method [7]. The first of them provides illumination of the observed scene by narrow-band pulsed laser radiation, and the second-the formation of the resulting images. The control of the stand, as well as the visualization and processing of the received data, is carried out on a personal computer using an application specially developed for this purpose.
When the scene is illuminated, part of the radiation reflected from the RRO returns to the light source, creating a light response (glare) of significantly greater intensity than in difuse reflection from other objects. The location can be made both by single-frequency pulses, and their series with the accumulation of signals by the CCD receiver of the photodetector.
The stand allows you to detect with high confidence RRO with a small light aperture at a significant range. As an example, Figure 1 shows an image of a scene containing a camera (the lens focus is 50 mm, the aperture value is f/16) on a natural background (soil cover, tree-shrub and herbaceous vegetation), recorded using an optoelectronic stand with the accumulation of a signal from 100 illumination pulses.
It can be seen that the detection of RRO with such a small retroreflective index in this image can be performed accurately due to a significant increase in the signal-to-background ratio, even without the use of additional image processing. However, as practice has shown, with intense sunlight and the presence of objects of artificial origin in the field of view, the task is significantly more complicated.
To increase the eficiency of the detection of RRO under these conditions, it is proposed to use periodic illumination of the scene, and at the stage of data analysis to perform inter-frame processing [8] of the recorded images and search for spatial anomalies [9] by a series of 2 frames of the sequence, where is the number of frames with and without illumination.
Cross-frame processing is designed to form diference frames and is performed in two ways. The first one is implemented by a simple pixel-by-pixel subtraction of the frame with illumination and the frame without illumination [7].
(, ; ) = 1(, ; ) − 0(, ; ).
(
The second one is proposed to be carried out by pre-replacing each pixel of the image obtained in the absence of laser illumination with the maximum value for its given neighborhood, i.e., by local processing.
(, ; ) = 1(, ; ) − max [0(, ; )] .
In this case, the size of the neighborhood must correspond to the estimated size of the images of the detected objects.
At the final stage of inter-frame processing, it is advisable to perform averaging of a series of diference images obtained in one way or another (, ; ) = 1 ∑︁ (, ; ).
1 =1
The procedure for searching for spatial anomalies from the generated diference images (, ) includes: — formation of the spatial distribution of the informative signal (, ) = ︃( 1 ∑︁ ( + 1, + 1) − Ω Ω 1 ∑︁ ( + 1, + 1) · )︃ where and are the central and peripheral zones of the fragment, and Ω are the number of pixels in them, respectively, 1, 1 ∈ , Ω ; — determination of local maxima in the resulting distribution (, ) by processing with a sliding window where 1, 1 ∈ .
(, ) = 0,
else
︂{ (, ), if (, ) > max ( ( + 1, + 1))
(
Next, in the array (, ), the mean and the standard deviation of non-zero values are calculated, i.e., the parameters of local maxima typical for this scene are determined (a description of the “background” class is formed), and the anomalous elements of the array (, ) are selected using threshold processing (, ) = ︂{ 1, if (, ) > +
0, else where is the threshold coeficient, which is usually 3 (it can be selected by the operator depending on the observation conditions). The position of non-zero elements in the binary array (, ) corresponds to the coordinates of the detected objects in the original image (, ).
To confirm the prospects of the proposed method of data processing, experimental studies were carried out on the detection of RRO in conditions of intense background radiation with the use of periodic illumination by a series of pulses and the formation of diference images. The measurements were carried out in the daytime under bright solar radiation. A number of retroreflective and mirror-reflecting objects were located in the field of view at diferent ranges. Among them are two RROs that are the target of detection: a television camera and a road sign with a retroreflective coating, located on the roadside at a distance of 1800 m. At the same distance, there were two objects that mirror the sunlight in the direction of the recording system (car elements). In addition, there were several other bright objects in the foreground and background of the sctne, including those with a reflective coating. Figure 2 shows images 1(, ) and 0(, ) containing the specified objects registered in the presence and absence of laser illumination.
It can be seen that in these images, the detected objects are dificult to distinguish without performing special processing.
In the diference image (Figure 3, a), formed without accumulation according to
expressions (
a b Figure 3: The diference image ( a) formed from two frames registered in the presence and absence of laser illumination, and the maxima of signals in this image by columns (b). a b Figure 4: A diference image accumulated over 20 frames, with the result of detection for pixel-by-pixel processing (a) and the maxima of signals across columns (b).
Performing procedures (
The diference image (, ), formed according to (
To suppress them, it is proposed to use the formation of diference images with a preliminary
substitution of each pixel of the image obtained in the absence of laser illumination for the
maximum value in its given neighborhood in accordance with (
In Table 1 given the relative signals of the objects in the resulting diference images for three
ways of their formation: I — in accordance with the expressions (
The obtained data confirm that the accumulation of frames with the preliminary use of
the procedure for determining the maximum value for the neighborhood in the background
frame allows you to additionally (several times) increase the relative signal level of detected
objects (
Algorithms are proposed and hardware and software tools are developed for complex intra- and inter-frame processing of image sequences for the detection of retroreflective objects based on the search for spatiotemporal anomalies using active pulse location by optoelectronic means.
Their eficiency is demonstrated for the selection of RRO in conditions of intense sunlight in the presence of mirror and difusely reflecting surfaces in the field of view, the signals of which are transmitted through the use of the proposed methods of digital data processing. Thus, the signals of static objects are suppressed by pixel-by-pixel inter-frame subtraction, and dynamic objects are suppressed by the accumulation of diference images.
Of particular note is the method of forming diference frames with the preliminary replacement of each pixel of the background images by the maximum value in its neighborhood. Its use makes it possible to suppress the signals of relatively slow bright dynamic objects that shift by no more than the size of the object during the recording of a series of images.
The work was carried out with the support of the Ministry of Science and Higher Education as part of the implementation of the State Task No. 121022000116-0 in the Institute of Automation and Electrometry of the Siberian Branch of the Russian Academy of Sciences.